yy," 1c1~n62 - Central Institute of Brackishwater Aquaculture

1996 

NATIONAL WORKSHOP CUM TRAINING ON 

BlOlNFORMATlCS AND STATISTICS IN AQUACULTURE RESEARCH 

February 2 - 5. 1 

S. AYYAPPAN 

DIRECTOR 

A.K. ROY 

COORDINATOR 

I 

Sponsored by 

DEPARTMENT OF RIOTECHNOLOGY 

Ministry of Science & Technology, Govt. of India 

and 

- 

yy," 

I CENTRAL INSTITUTE OF FRESHWATER AQUACULTURE & J INS4 ~ 

\Sf1 4 r, 

Indian Council of Agricultural Research 

s 

Ti 77 1c1~n 

!CAP Kauzalyapanga. Bhubanecwar-75 1002, Onsca, lND1A @-

NATIONAL WORKSHOP CUM TRAINING ON 

BIOINFORMATICS AND STATISTICS IN 

AQUACULTURE RESEARCH 

BIOINFOIUMATICS DIVISION 

DEPARTMENT OF BIOTECHNOLOGY 

Ministry ofscience & Technology 

Government of India 

New Delhi 

BIOINFORMATICS CENTRE ON AQUACULTURE 

CENTRAL ~NSTIWTE OF FRESHIYATER AQUACULTURE 

Indian Council ~~Agricullural Research 

Coordinator: A. K. ROY 

Director: S.AWAPPAN 

CENTRAL lNSTlTUTE OF FRESHWATEIt AQUACULTURE 

(Litdian Council of Agricultural Rrsearcl~) 

Kauulyngrngr, Bhubaatmrr-751002, Oriasa

FOREWORD 

Since the adkt ofmodirn science, attempu have been mad to impmve the 

speedandefincy ofscientrfit communication Most of th schohrfj infonnatton 

howewr, fi 

cotltirlued to 6e puGfulied in print, it., in jounlali, 6006, cot$ermce 

proceedings etc. Ihc emergence of the Internet is radlua$ chattgi~tg the dentratiorr 

flow ojutllisation ofinjonnatwngIbbaQ. 

Wth the advent of information age, major initintives have been taheprc by the 

Indian CounciC$~gricuCturalQseanli (Iu@ to modntiee attd6nng information 

ntanagement cuCture in aa areas of&ncufturaC Qseatcli. '&epittg in view the 

06jectives ofIu$ CIFJ is aalro engagedin the ttas[ofmodintizing the hardware and 

softuare itutalTations in ordrr to cope with the (ntsst developed 

information 

technolbgics. Wth tie impkmentation ofuy cottnectiw'ty, it L possi6l for the 

Scientists to share common rtsources f& 'VSJls, Laser Rinters, Statistical%c&ges 

andData6a.s~. ata66hrncnt ofrSwinfomaticJ Centre on JquacuCture at CIFJ 6y 

QlotechnoJbBy Ir$onnatwn System (BIIS) of Department of Qiotechnobgy, Ministry 

ofscience andlcchnolbgy, Gwt. of India fa h[pedu~ immensely to stretrgtlien our 

system which wrlSsun(y boost th 8&D efJoort~ in thfielii offislieries science iir 

genera[ and JquacuCture in particuhr. Jpart fmm ofline Gibfiographic fiterature 

search thmllgh WM, the globalinfonnation onfinr highway [nown as internet 

can 6e ued6y aascientists with internet connectioru to see adeqlbre thowad of 

cihtabases stored there. 

llir prarent worhhop is Lsigned to introduce the participants to the 

interesting wodfof &ta communication, data6ases, intenlet, muftirnediu, homepage 

rkvelbpment, statistical methodolbgics and packages and their application to 

aquacuftutz rueanh. lie epenetue aained from this workhop-cum-training 

programme, d enabb &nt$cation of spec* applications in dizerent 

mvironmenu. I tab this opportunity to thankthe participants, organizationr andan 

othn WKO haw contn6utcdto this worbhop for itJ success. 

S. AWAPPAN 

DIRECTOR

Bioirlforntatics Cetrtre, CIFJ expresses its sinceregratitude to Dr. 3. '1(, Jmra, 

Jdviser, Departntent of Biotechnobgy, Nittistry of Science arid kchnology, 

Goventmetit of I11diu for his coiutant advice andertcouragentent for devebpntent of 

this Bioitlfonnatics Centre otr Pquacufture. Yfea~tful thanb are aGo due to Dr. 1: 

%fad7ianmohan, fi~'tic$al Scientijc Offirer, Wl for his continuous touch arid 

support for impruuement oftfiu centre. 

lfie Centre u indebtedto agth resource persou ofvarious orgarrisatiorrs fik 

CIPW, WD/1, C W , STPI, MC, IGjIU, Cakutta Utriversity, ISI, Ut&d 

University, @criiampur Vrriversity, State (Tisheries (Omsa oZ WB), IG%o'L) art6 

ClFJ for cotrtriiution andpresentatiolr ofpapers andexchange oftheir vafwGlk tdeas 

with the participants to ma@ this "Won$iop-cum-Training programme" a grand 

success. 

Qioirlfomatics Division, Department of Qiotecfirrolbgy, Gover.rrnterrt of ltrdi~ 

is gratefilto Dr. S. &yappan, Director, CIFJ forprmdirrg aa thefacifities to thu 

Qioinformatics Centre to qecu:ccutc aa its oi)ectives hid down by Biotechnoby 

Infonnatwn System (BIIS) ofW1; Nw Deffii

Director 

Dr. S. Ayyappan 

Coordinatoc 

Shri A. K. Roy 

Associates: 

Shri P. K. Satapathy 

Shri D. P. Rath 

Shri Ramesh Dash 

Cover photo : VSAT inslalled et roof top of ClFA

CONTENTS 

1. Status of Bioinformatics Centre on Aquaculture 

- A. K Roy & S. Ayyappon 

2. Internet and the lntranet 

- Manas Patnaik 

3. The World Wide Web & Information Searching 

- Bikash Panda 

4. Internet and die Emerging Networked Society 

-A. K Roy 

5. Establislinient of Local Area Network and Internet under the 

ARISNET: A Case Study 

- G. R Marulhi Sankar 

6. Putting Education Online: A Case Study 

-A. R Tl~akur 

7. Web Site Design & Hosting 

- Bikash Panda 

8. Multimedia - a magic mantra 

- Jayaram Parida 

9. Multimedia - on the Web 

- Jayaram Parida 

10. World Wide Web, the lnformation Store House 

- B. K. Panda, A. K. Nayak, A. KRoy & P. K. Satapatly 

I I. 

Designing and Planning your Database 

- Swya Kumar Parranqvak 

12. Database on Fish Disease 

- B.B.Sahu. A.KRoy, P.KSaiapafhy, S,C, Mukherjee and S. Ayyappan 

13. Quantitative and Qualitative Fish Production Database 

- B.B.Sahu, J.X. Jerta, A. X Roy, & S. Ayyoppan 

14. Database of Induced Breeding Experiments on an Indian Major Carp 

hbeo rohira (Ham.) 

- S.D.Guprcr. A. K. Roy, S.C.hrl~. P. K.Saraporhy 

15. The Millennium Bug or the Y2K War 

-A. K Roy

STATISTICS 

Scopc of Applicnlion of Statistical Mcthodologies in Aquaculture 

I\escarcl~ 

A. # Roy 

Many Faccs of Slatistics 

- A!. Nour 

I:unda~uentals of Sa~npliny and its Application in Fishery Resource 96- 107 

Snnlplir~g Tccl~niques Applied in Assessi~lg Inland Fishery Resources I08 - 1 18 

and I'roduclion 

- H. A. Guplcr 

Corrclal~ons and llegressions 

-A I! Suryu Roo 

011 SOIIIC Slilt~st~cal I'rocedurcs for A~~alysis or Data from Field 128 - 135 

Expcrimc~its 

- G. R. A4arull1i Sartkar 

I:uridanler~tals of Design and Analysis of Field Experiments with a 

Note on l'ransfonnation of Data 

Rmri R. Sare~rand A. K. Roy 

Advo~iced Statistical Methods for Dab Analysis 

- R. N. S~~burliri 

AII Overvicw of Statisticnl Packages 

- Ravi It. Snre~to arrd A. K. Roy 

EXCEL for Smtistical Data Analysis 

- P. K Surnparl~y, A. K. Roy and R Dm11 

Ins~ructions for Operating Minitab Statistical Package 

- Srabashi Das~r

STATUS OF BIOINFORMATICS CENTRE ON AQUACULTURE 

A. K. Roy and S. Ayyappan 

B~oinfonnatics Cenlre 

Cenlml lnsblute of Freshwaler Aquaculture 

Kausalyaganga. Bhubaneswar 

BIOINFORMATICS, STATISTICS AND INFORMATION TECHNOLOGY 

The term 'Bioinformatics" refers to the area of interaction between the 

information technology (IT) and the Life-Sciences including biotechnology. Again IT is a 

convergence and integration of three main technolog~es taken together viz., Computer. 

telecommunication and microelectronics. Further to trace the connection between a 

statistics and information technology, ~t is necessary to go back to h~s royal society 

address delivered many years ago when famous statistician Maurice Kendall quoted 

"Statistics, is indeed, not confusion but fuslon, a sort of unified whole, the matr~x of 

quantitative knowledge of nearly every kind, the pr~nclpal instrument yet devtsed by 

men for brtnging within his grasp the complex~ty of things". He elaborated that just as 

statistics per se was the totality of information, the technology of statistics was nothtng 

but the totality of technology of information or information technology. He further rtghtly 

professed that the era of computers would only be heralded by future generattons of 

statisticians. W~th the entire cosmos as one cybernet~c entity, the umly~ng disc~phne of 

statistics and information technology now appears to be a reality Presently, it is 

emphasised the need to use the Markov Chain Monte Carlo simulat~on techn~que in 

order to improve the quality and reliability of computer software. 

Bioinformatics gained a new dimension when 11 was understood that all the 

biological processes depend on genetic information stored as linear codes along 

gigantic chain molecules. It provided the structural and functional information on macromolecules 

and development of mathematical models that illustrate the dynamic 

interaction within and between cells. The advantages that will come from finding the 

right solutions to the questions posed by the interaction of biotechnology and IT are 

unlimited. Various activities of bioinformatics would be creation of databases either 

bibliographic or containing properlies and results; access and retrieval of information 

from databases either on line or off line: analysis of information which may be either 

empirical model building based on various results of experiment or literature surveys 

and training. The need for bioinformatics started gett~ng attention due to gradual 

realisation of the fact that the basic and applied research in the areas of Life Sciences 

and Biolechnology is becoming increasingly dependent upon an understanding of the 

Biological processes at the molecular level Moreover it is felt the need for applying 

computer based analytical tools to the huge biological data accumulated over the past 

and sharing the data among workers and synthesizing information from isolated 

literature references. It is well known that a database provides information for surveys,

prevents duplication of works, cross veriiy experiments and predicts common 

characteristics, and helps writing of research papers, project proposals, etc. Due to its 

importance, Departrnent of Biotechnology started the Biotechnology Informatics 

Systems to provide an informatics based national infrastructure in the form of a 

distributed database and network organisation for harnessing the scientific knowledge 

in various interdisciplinary areas of biotechnology and its dissemination to scientists 

working in RBD organisation. BTlS has been established to serve as a distributed data 

base and network organlsation. It is comprised of nine specialized distributed 

information centre (DIGS) in six identified areas of Biotechnology (Genetic engineering. 

Animal cell culture and Virology, Plant tissue culture, Cell transformation, Nucleic acid 

and protein sequences, Immunology, and Enzyme engineering), nineteen Sub-DlCs for 

distribution of scientific information across the network. Another 15 Sub-DICs are in the 

process of establishment located at different national institutes and laboratories. The 

principal objectives of the DlCs is to function as an information base In each speciality, 

to provide a computer based information storage and retrieval system of databases, to 

provide retrieval service either online or offline, to provide communication I~nk, to 

develop software packages specific to user needs and to conduct training courses in 

the specialised areas for manpower development, to promote awareness about the 

computerised storage and retrieval facility among bio-scientists and information 

scientists. 

DEVELOPMENT AND MAJOR ACHIEVEMENTS OF BTlS ON AQUACULTURE 

The Bioinformatics Centre established at Central lnstilute of Freshwater 

Aquaculture. Kausalyaganga, Bhubaneswar is a Distributed Information Sub-centre 

(Sub-DIC) under Biotechnology Information System (BTIS) Network of the Departrnent 

of Biotechnology, Government of India during 1991-92. The centre specialises in the 

field of aquaculture and serves as an information source in the country. 

Infrastructure and physical facilities developed 

The BTlS being an informatics based infrastructure required special attention for 

right selection of computers and communication systems. Procured the following 

essential hardwares and softwares and distributed to different Divisions of the lnstilute 

for use of the Scientists and Research workers using LAN connectivity with Server at 

BTlS room. 

Hardwares : 

486 Computers (9 nos.). Pentium (26), Macintosh SE (1). Multimedias (2). 3 

KVA UPS (I), Server (I), Dot Matrix Printers (lo), HP Deskjet (15), HP Laserjet (4), 

LCD Projection Panel (I), Colour Scanner (2), Fax machine (I), Modem (2) and VSAT 

(4).

Wldows 95. UNIX. MS-Office. Novel Netware 4.1, SPAR1. SAS, FOXPRO and 

QPRO. 

Creation and Procurement of Databases, Databank, Aquaculture Dlrectoriea, etc. 

Databases: Created the following databases related to aquacultural activities covering 

statistics, bioinformatics, resources, bibliography, nutrition, pathology, 

meteorology, biodata of Scientists and other activities related to aquaculture. 

a) Database on Freshwater Fishes (Textual) 

b) Database on Freshwater Aquatic Plants (Textual) 

c) Database on Fish Disease (Textual) 

d) Database on Fish Pathology (Bibliographic) 

e) Database on Fish Nutrition (-do-) 

f) Database on Aquatic Microbiology (-do) 

g) Database on Institutions and Companies working in the field of fishing 

technology and aquaculture (Textual database supplied by FAO) 

h) Database on Suppliers and manufacturers of fishing technology and aquaculture 

equipment (Textual database supplied by FAO) 

i) Individual experts in the fields of fishing technology and aquaculture (Textual 

database supplied by FAO) 

j) Personnel Information System (PIS) obtained from /CAR 

k) A databank has been created at the centre incorporating the factual figures on 

fish production statistics of all varieties, species, water area available, etc. for 

different states alongwith other 168 items on agricultural products i.e. rice, 

wheat, potato, cotton, maize, etc, and Animal Husbandry products i.e. egg. 

meat, milk. etc. This has facilitated the supply of information to users 

besides the information on fisheries. 

I) Aquaculture Directorlea : Aquaculture Directories have been prepared which 

cover detailed information on addresses of Educational and training 

Institutes in different countries along with courses, programmes, feed 

manufacturers, exporters, address of services, consultants on 

aquaculture, capture fisheries, fish processing and fisheries information 

services for literature on films, videos available in different countries. A 

directory covering all universities in India, ICAR, CSIR, Fisheries Directors 

and National Research Centres and Project Directors is also available at Ulis 

Centre.

m) Acquired CD-ROM on ASFA and CD-ROM on Fish Base for facilitating offline 

bibliographic search by the Scientists and Technicians of the Institutes around 

Bhubaneswar and also other ICAR Institutes, Universities and Fisheries 

Colleges engaged in Research, Training and Teaching activities. 

Software development 

More than 35 programs In Fortran 77 and FOXPRO have been developed for 

statistical data analysis and information retrieval respectively. Some of these are 

ANOVA, Probil Analysis, Multivariate analysis, fish growth, length-weight analysis, 

Split Plot design, DMRT, Heterogeneity test along with no. of statistical test programs. 

Programs have also been developed for library management system, paybill, etc. 

Network Linkage 

The centre has acquired a VSAT for e-mail uploading and downloading. 

Micro Earth Station w~th C-200 controller has been installed at ClFA from Nov., 1995 

and E-mail facilities both national and international have been provided to the Institute. 

It has also a MODEM connected through telecom to NICNET to access databases 

developed by NIC i e. GIST, RENNIC. SLlPlPPP Connectivity for internet browsing has 

been acquired by the centre for online search of information. 

Library and Office Automation 

The library system is under computerisation. CDSIISIS package is used for the 

authonuisel titlewiseldiscipiinewise search for entire books available in the librav. This 

has facilitated to a great extent for the search on availability of library books at the 

centre. 

Manpower Development through WorkshopITrainingfreaching 

Studentship 

and offering 

The following Workshops and Training programmes were conducted and 

studentships offered by the BTlS centre on Aquaculture for extending information 

related to aquaculture and role of information technology (IT) on aquaculture 

development using modem tools. 

a) National Workshop on Perspectives in Bioinformatics and Its Application 

to Aquaculture was conducted during February 22-26, 1994. 

b) National Workshop on Networking and Biological Data Analysis was 

arranged during February 4-6, 1997. 

c) National Workshop on Information Technology in Aquaculture Research 

was arranged during February 10-13, 1998.

d) Students of Orissa University of Agricultural and Technology are being 

regularly trained on the use of Computer Application in Aquaculture 

Research in their Master of Fisheries Science and Ph. D, courses apart 

from periodical training of the Scientists of CIFA. Teachers. 

Researchers of Utkal University, ICMR, RRL, Regional College of 

Education as well as workers of other lnstitut~ons also avail this facility. 

e) Several training programmes were also conducted for staff 

members of CIFA. The centre has also conducted many training 

programmes for officials of State Fisheries, different colleges and 

universities of Orissa. 

f) Regularly students are trained in Bioinformatics offercng studentship 

under BTlS project. 

lnternational Collaboration 

The centre is collaborating with lnternational Development Research 

lnformation system (IDRIS) of IDRC, Canada for obtaining information on fisheries 

activities located in or concerned with developing countries in diskettes which are 

updated by them every six months. This centre is selected by the Fishery Advlsory 

Services (INTI86/D12) of FAOIUNDP, Rome for dissemination of information on 

fisheries and its allied disciplines through the diskettes prepared by them The centre 

has also received CD-ROMs on Fish Base from ICLARM, Philippines, which provide 

databases on fisheries, particularly for fishery research workers. Maxims. Ecopath 

and Fish growth parameters packages have been collected and are being utilised. 

Future Programmes 

LAN service will be upgraded, KU Band VSAT is intended to be procured for 

best use of lnformation Technology in Aquaculture. This system will help in providing 

electronic bulletin and e-mail to the scientific and technical personnel independently 

by using existing VSAT as well as dlal-up MODEM A remote login system is 

proposed to be developed lo give an access to all Bioinformatics Centres, ICAR 

Institutes and other research organisations. This remote login system will help to 

share the information generated here amongst research organisations. Creation of 

CD-ROM on databases developed at the centre will be distributed to other research 

organisations for off-line search facilities. Attempts will be taken to prepare menudriven 

software packages for carp culture, prawn culture, catfish culture, pearl 

culture, paddy-cum-fish culture, etc. which will guide the entrepreneurs for taking up 

the aquaculture independently. Physical, chemical and biological parameters of fish 

ponds will be monitored from the model to be developed during this period. CDNET 

facility will be developed in LAN system lor sharing of bibliographic search by 

researchers and Scientists of the Institute. Training course for training researchers in 

the field of Bioinformatics is being taken every year.

INTERNET AND THE INTRANET 

Manas Patnsik 

Director, 

STPI-Bhubaneswar 

So what is the difference between the lntemet and lntranet 7 

Mainly the location of the infomation and who has access to fi 

lnlemet IS public, global and wide open to anyone who has an lntemel connection. The 

Internel is a phenomenon, created by the physical connection between thousands of prlvate 

networks. Like the phone system. the Internet allows instant communication between any 

two points on a network, lnstead of connecting phones, however, it connects computers. 

Instead of voice and fax, you are exchanging digital information, including: 

Documents 

Data 

Multimedia (recorded video, audio) 

lnlranets are restricted to people who are connected to the private company network. Other 

than that, they work esseritlaily the same way. lntranets can help empower their employees 

thtouph more timely and less costly information flow This empowerment bolsters a 

company's competiliie advantage, through improvement of employee morale and asssting 

in gelling more timely information to customers and supplien. 

Wille 1995 was clearly ttre 'year of the Internet'. 1996 is being termed the 'year of the 

Intranel'. 

lritemet technologies llnplemenled internally wer dlentlse~er networks are called Intranets. 

lnlranets can operate behind firewalls in conjunction with lnlemet access, or be 

implemented exclusively as internal distributed networks over LANs and WANs. 

A key fact to undersland is thta the lnlranel can work on any local area network (LAN), but 

really provides its greatest power on wide area networks (WAN). All companies began their 

network activities using LANs, but the plummeting cost of network connections now makes it 

increasingly affordable to connect all the far-flung LANs into a single, integrated WAN. Most 

network computer applications are geared to the LAN, whereas internet applications were 

originally designed to be used wer a WAN. Because of this WAN capability, the intranet 

makes il possible to connect any user in the company's wide area network to any web site 

located on that network. So, for instance, if your company has internal web sites in London, 

Singapore, Seanle, and Information from any of those sites with equal ease. 

lntranets present a less challenging development environment, so that many organisations 

preler to lmpiemenl tntranets first, perhaps with a modest, network isolated Internet site. 

before a full blown, firewall protected lntemel sile Is contemplated. 

Some Key dlfferencss between the lntemet and lntranet are :. 

INTERNET 

Client toots divene 

Browser compliance an issue 

. Client connection speeds vafiable. 

Users have divene skills sets 

Animation. video reslrided. 

Mintmat implicalions for work-flow

Can standardize client tools 

Bmwser compliance generally not an issue. 

Network speed standardised 

Users can be trained 

Full multimedia oflen possible 

8 Implications for work-flow end process re-englneenng 

How can inlranets save time in a corporale environments 7 

Wflh corporations under tremendous pressure to empower employees and lo better 

leverage internal information resources, inlranets are being seen as the solutions. 

A basic intranet can be set up in hours or days and can ultimately serve as an 'Information 

hub' for the entire company, its remote offices, parlnen, suppliers and customen. 

Key differentiaton that distinguiosh lntranels as the future medium for corporale internal and 

external comunications. 

Freedom of choice 

Ease of Use 

Cost effed'weness 

Richness 

Powerful tool for sharlng infonnation across networks 

Merges documents, data and mult~media 

Universal access 

Universal interfaces to all file system 

Totally in-house. protected from publlc security (i.e IntemeVwww) 

How does one authenticale user lo make sure they are who they claim to be 

How can one perform authentication without send~ng user names and passwolds across the 

network in the clear 

How can single user log in services be provided to avo~d costly user name end account 

maintenance for all the servers (web. Proxy, directory, mail, news, and so on) across the 

enterprise 

How can one protect the privacy of communication, both lhose in real time (such as the data 

flowing between a web client and a web server) and those with store-and-folward 

applications such as e-mail 

How can one ensure the messages have not been tampered with between the sender and 

the recipient 

How can one . .eguard wnfidenttal documents to ensure that only authonsed indivlduais 

have to awe to them 

Today. the, is a single technology that provides the foundallon for soking aH these 

challenges: Ctyplography. These standards provide the foundation for a wide variety of 

sewrfty services, including encryp(lon, message integrity veritlcation, authentication and 

digiial signatures. 

Encryption transforms data into some unreadable form to emre prtvacy. It Is the dlgnal 

equivalent of a sealed envelope. 

7

Decryption is the reverse of encryption, it transforms encrypted data back into the 

original, intelligible form 

Aulhenticat~on idenlines an entity such as an individual, a machine on the network, or an 

organlzatlon 

Digital signalures blnd a documenl to the possessor of a particular key and are the 

d~gital equivalent of paper signalures 

Signature verificalion is the inverse of a digital signature. A verifies that a particular 

signature is valid. 

INTRANET APPLICATIONS IN A CORPORATE ENVIRONMENT 

Some common appl~calions are : 

Sales and marketing applications 

1. Product specificallons, price lids and new collateral 

2. Sales Leads 

3 Competitive informallon 

4. Lists of key cuslorners wins, including winlloss analysis 

5 Online training rnalerials 

8. Sales presenlallons 

Product development appllcallons 

1 Product spec~ficaltons, destgns, schedule mtieslones, and charges 

2 Team member llst~ngs and cespons~bld~es 

3 Cuslomer Issues 

4 Features of key competlt~ve products 

Cuslomer service and support applications 

1. Share the latest reports on problems so that any team member can respond to 

customer calls 

2. Get the current information on the status of cuslomer's orders 

3. Be alerted lrnnlediately lo any important changes such as special offers or issues 

4. Traln onllne to respond lo customer queries and complaints 

. Human resources appllcal~ons 

1. Company mission and goals 

2. The annual repod 

3. Searchable telephone directories 

4. Job poslings and internal job transfer forms 

5. Employee development 

0. Departmental and personal home pages 

7. Classified bulletin boards of items for sale, housing etc. 

8. Medical referrals 

9. Online employee enrollment in specific benefit plans 

10. Employee surveys 

11. Employee lookup of vacalion balances, oplions elc and 

12. Ollllrle submission of employee status change 

FINANCE APPLICATIONS 

Wdh inlranel a~~lications. finance de~arlments can disseminate information to key 

manages by &curely posting corpoite financial data or by providing simple form:based 

query capabilllies. The purchasing site of financial operations can also benefn from intranet

OTHER APPLICATIONS 

Numerous other corparate departments such as legal or MIS groups currently ustng paper 

based forms or polides can reap the benerns of making transaction a~Dlicalions available 

through intranets 

ELECTRONIC MAIL AS A PART OF INTRANET 

When a person takes an internet from the ISP (Internet Sewlce Providen) the e-mail 

address will be that of the ISP. It is like ustng a business center for an office Say ClFA has 

taken service from STPl Bhubaneswar, then their mail address w~ll be ~j~$c&!i..$~D~~c_l. 

The above do not present serious options to a corporate organisations. 

For its employees to use e-rnail, the corporate can gtve an address like IL!~IIW~~ITJI$S~~ 

indicating the name of the organisation/corporate Cifa as a research organisation in Indta. 

SUMMARY 

The Internet has not only brought about a technology revolution, but it is also taunchtng a 

second revolution in corporate computing The internal use of lntemel software has become 

known as the 'INTRANET. For India, Internet is a great opporlunily. Although currently we 

do not have more than 50.000 lntemet conneclion but it has already caught irnaglnatlon of 

the people. The numben of usen are estlrnated to be more than 2 lakhs Undoubtedly 

Internet has emerged as the largest non-stop talent show lntemet business in India Is likely 

to fetch revenue of more than Rs 70 billion by theyear 2000 Wflh Its low entry barrier and 

high intellectual opportunity, the intrarlet is of stgnificance for organisatlons In Ind~a. A 

standard part of any business internet connection is the firewall, wh~ch keeps internet users 

from connecting into the company's private internal network If company has its own 

internal web sites on the internet, people on the internet will not be able lo see them without 

specla1 access authority.

THE WORLD WIDE WEB 8 INFORMATION SEARCHING 

Bikash Panda 

HIG-188, Kanan V~har, Bhubaneswar-751031 

World Wide Web 

The World W~de Web (WWW) is one of the most popular client-sewer based 

Internet services. In the late 1980's. CERN (the European Lab for Particle Physics) 

began experimenting with a service that would allow anyone to easily access and 

display documents that were stored on a server anywhere on the Internet. To do this, 

they developed a standard format for the documents that enabled them to be easily 

displayed by any type of display device, and allow links to other documents to be 

placed within documents. 

Although the WWW was developed for the CERN researches to use, after the 

service was made public it became tremendously popular. A number of different client 

applications (the ones that actually display the documents on-screen were developed to 

read WWW documents. There are graphical-based clients (one of the most popular of 

these is Netscape), and term~nal-based clients such as Lynx. Most WWW clients also 

allow you to use the same interface to access other lnternet services such as FTP and 

Gopher. 

Accessing WWW 

To use WWW you just require lnternet connectivity & preferably a graphical 

browser. The most popular browsers are Netscape Navigator. Microsoft lnternet 

Explorer. If your computer is properly configured to access lnternet using TCPllP 

protocol, then you can start browsing the W using your browser application. You 

need to know the Web Site address which you desire to view. This Web Site address is 

known as URL which stands for Uniform Resource Locator 8 it has the following 

syntax. 

An example of URL is Error! Reference source not found. This means you want to view 

an HTML document called default.htm available at the Web Server Error! Reference 

source not fouud. using HTTP (Hyper Text Transport Protocol). The name of the sewer 

is called the Domain Name which is unique worldwide. The Top Level Domain ('in" in 

this case) decides what type of Server that is. IN means that particular Web Sewer is 

an Indian Domain. Every country worldwide had this type two letter country domain. 

International domains are three letter ones.

.COM is for Commercial Organisations 

.NET is for Networks or lSPs (Internet Service Providers) 

.ORG is for Non-commercial orgnisations 

.EDU is for Universities or Educational Institutions 

.INT is for International Ongaisations 

.MIL is for Military Organisations 

.GOV is for Government Site 

Out of these top level domains .edu, .~nt. .mil & gov are only for USA based 

organisations. 

World Wide Web Authorities 

No body owns Internet & hence there is least numbers of controll~ng bod~es Thls is 

what makes the W so popular & masslve, IANA (Internet Assigned Names 

Authority) is the USA based Organisat~on which assigns Umque IP address for Web 

InterNlC (Internet Network Information Centre) manages the Domain Name regstration 

of International domain names. More details can be found at Error! Ilelerence aource 

not found. An organisation World Wide Web Consort~um sets the standard of WWW 8 

HTML tags. Their details can be found at www.w3c.org 

Information Searching 

Nobody expects you to remember every possible s~te names & browse accordingly. 

One has to search the sites which might be hav~ng reference to the Keyword you are 

searching. For this purpose special Websites called Search Engines available The 

most popular one is www.yahoo.com 

The following is a 11st containing various URLs for variety of purposes 

Search Engines 

www.yahoo.com 

www.altavista.com 

w. hotbot.com 

www.infoseek.com 

www.khoj.com 

Free E-mail Service Providers 

www.hotmail.com 

www.rocketmail.com 

mail.yahoo.com 

www.mailcity.com 

www.excite.com 

www.usa.net 

www.lycos.com 

www.excite.com 

www.search.com 

w.webcrawler.com 

www.web-search.com 

Oniine News Sites 

w.timesof~ndia.com 

www.expressindia com 

w.sarnachar.com 

www.asianage.com 

w.aajlak.com 

w.hinduonline.com

www.poeox.com 

www.letterbox.com 

www.juno.com 

People Finder Sites 

www.four1 l.com 

www.whowhere.com 

www.alumni.net 

w.batchmates.com 

Free Web Hosting 

www.geocities.com 

www.angelfire.com 

www.xoom.com 

www.forlunecily .corn 

www.tripod.com 

w.cnn.com 

w.hindustantimes.com 

w.economictimes.com 

Job Providers in Internet 

www.naukri.com 

w.winjobs.com 

w.dice.com 

www.careerpath.com 

www.bestjobsusa.com 

w.ciol.com

INTERNET AND THE EMERGING NETWORKED SOCIETY 

A. K. Roy 

B~ornforamabcs Centre 

Central Institute of Freshwater Aquacuffure 

Kausalyaganga, Bhubaneswar - 751002 

INTRODUCTION 

In the simplest form Internet is the network of networks. Internet (known 

as Net) is the world's largest computer network. A computer network is generally a 

bunch of computers hooked together somehow for exchanging ~nforrnalion freely. It 

is a new communicatton technology that is affecting our llves on a scale as slgnlficant 

as the telephone and television. It is a worldwide computer network connecting nearly 

5 million computers around the world. There is no censorship. Probably that is one of 

the reasons of its popularity and exponential growth. 

COMPUTER NETWORK 

Computer networking refers to a method is which the computer systems are 

connected together is such a way that they can exchange informallon among 

themselves. They can be connected by wires, phone lines, satell~te llnks or any 

combination of these. Each computer network has a host computer, known as 

server, which controls the complete network. If networking is done in the same 

bullding or in small area, it is known as Local Area Network (LAN), if the computers 

are spread over the metropolltan Area then it IS known as Metropolltan Area 

Network (MAN). When the computers are spread over larger area, the network is 

called Wide Area Network (WAN). Networking IS done for sharing resources like 

printers, hard disc drive and software 

SOME INDIAN NETWORKS 

NICNET, ERNET, INDONET, METNET, PRESS NETWORK, OILCOMNET. SIRNET, 

AIRLINE NETWORK, INFLIBNET. 

WHO USES INTERNET 7 

Once closely guarded by sc~entlsts and technocrats, today the lnlernet IS open 

to researchers, students, parents, poltce, buslnessrnen, world leaders, executives, 

sport fans, shoppers and terror~sts Internet is the largest and most complete 

learning tool for groups of people with varied educational backgrounds and 

interests.

SUBJECTS COVERED BY INTERNET 

lnternet covers almost all the subjects imaginable. Some of which are Arts 

and Culture, Books and literature, Business and Career, Computers and 

Software, Education and Teaching tools, Environment and Nature, Food and 

Cooking, Games and Sports, Government and Politics, Health and Nutrition, 

History, Household and Consumer finance, Humor, International affair, Language 

and Linguistics, Law, Movies and video tapes. Music, Religion and new age, Science 

and Technology, Space and Astronomy, Shopping. Sports, Recreation and Hobbies, 

Television, Travel and Geography and many more. 

LENGTH AND BREADTH OF INTERNET 

The information available on the internet has been indexed. If one reads 

only index pages at the rate of 100 pages daily, it will take 4 years to read the 

complete index only wh~ch is equivalent to 1,46,000 page. As per the latest report 

available, there are 2.2 million current users of internet and every month 1,50,000 new 

users are joining it. The internet has 40,000 host computers also known as web sites. 

It is estimated that by 2000, there will be 100 million users and 1 million hosts on the 

internet. 

NAVIGATIONAL TOOLS OF INTERNET 

The following are the navigational tools of internet: 

E-mail (electronic mail), File Transfer Protocol (FTP), Telnet Gopher, World 

Wide Web (Mosaic), Finger, Usenet, Mailing Lists (Listservers, Viewers, Archives, 

Encoding, Lynx, lnternet Relay Chat (IRC), Wais, Veronica, Bulletin Board System 

(BBS) and Free Nets. 

VARIOUS APPLICATIONS OF INTERNET 

lnternet has given access to an enormous amount of information. This 

information can be accessed and used from any comer of the world and knowledge of 

access tools is necessary to make maximum use of interenl. In India and all over the 

world the lnternet is being used for wide variety of purposes, only few are mentioned 

below. 

ELECTRONIC PAPERSIJOURNALSINEWSLETTER 

Newspapen and magazines are available on the Internet. Recently many 

Indian News papers have been introduced on the Interent. Many International 

Scientific Journals are available in the Internet.

MATRIMONIAL ALLIANCES 

Matrimonial alliances are being done through lnternet for which some 

companies have started matrimonial service site. 

PATIENT CARE SUPPORT 

lnternet is a continuously updated database for providing patlent care 

support and serves as a d~stant learning facility for student physicians. On-line 

medical journals, through which the latest research and development in the field IS 

known. 

INTERNET PHONE 

One can now-a-days place calls over the lnternet to standard phones or PC's 

running Vocal Tee lnternet Software along with placing calls via the internet. It 

gives lnternet users a vocal two-way communication facility. This lnternet phoning 

is now as simple as E-mailing or traditional phoning. The rate is lower than STDllSD 

calls. 

NET VARSITY 

Another Interesting thing is that recently NllT has establ~shed an on-line 

learning fac~lity on the Internet by the name of 'Net Vars~ty' based on the 

conventional model of a university. According to NIIT, the NllT varsity has all the 

features of an institution of higher learning including registration procedure, 

testing and certification. Other features include a library where the vast 

resources of the internet have been summarised, a student querylng service to 

offer tutor support to students, a student advisory service to provide counseling on 

learning opportunities and a placement assistance service The students will be 

eligible for certification for the education they get at the 'Net Vars~ty'. 

POSITIVE USE IN INDIA 

Government organizations l~ke CSIR, ICAR have set up Websile on the lnternet 

which gives information about their objectives, activ~ties and also about various 

labcratories. Department of Science and Technology Website informs about 

National Resources available for Science and Technology. NIC has a wealth of 

information on its Website.

DARKER SIDE OF INTERNET 

Due to the scope of unhindered use on uncensored subjects, it is being 

mlsused also in areas like pornography, nefar~ous and subversive activities by 

unscrupulous criminals breaking the database of banks, confidential records of 

defence establishments and secrets commercial rivals. Recently, there appeared 

news about the credit card fraud that hits the internet by school boy hackers. This 

computer scam fuels fears about shopping on the web. These are darker sides of 

internet which can not be ignored. 

A NETWORKED SOCIETY (NS) 

Communication technology is based on computers is computer mediated 

communication (CMC) which encompasses e-mail, virtual reality and computer game 

etc, Internet is a new way of using space and time. CMC provides a space - the 

cyberspace, within which forms a new society known as Networked Society (NS) or 

Cyber Society. 

Impact of Networked Society (NS) on the culture of people all over the world : 

1. W~th network spanning all over the world the convert of borderless nations is 

likely to be a reality. 

2. In the NS, the houses are likely to be the activity centre, not the office 

3. Less travel society if not a travel-less society 

4. Physical location may become irrelevant for develop~ng and receiving services 

5. Radical change in workculture due to flexi hours of working coupled with 

innovative management of resources and manpower resulting in enhanced 

productivity. 

G. Home centred act~vities would lead lo better creativity, innovation and 

product~vity. 

7. Telecommunication culture w~t home curbed activities would ultimately lead to 

home centred economy. 

8. Present society is characterised by community formation based on work 

centres. In a home centred environement, the communities will comprise of 

groups from among people pursuing different works and professions in life. A 

true social community is likely to emerge. 

9. The concept of association may vanish because in a networked society , small 

community dwellings which are self contained would emerge.

10. A networked society (NS) can be characterised by (anyone, anytime, 

anywhere, any information and any format ) 

11. A full-fledged NS implies that every human being on the earth has an access to 

network which is considered essential like elctricty and water. 

12. A poorest person from villages will have access to information resources in the 

richest in the cities. 

13. W~th round the clock operation of Newtwork infrastructions, tlme and holiday 

patterns may be irrelevant in the lifestyle of people. 

14. There communication technologies will play complementary roles There are 

optical fibres, sattelttes and short-wave radio which will provide bandwidth. 

qu~ck remote area connectivity and excellent last mile link respectively. 

15. Network computers and multimedia personal computers w~ll emerge. 

16. Virtual reallty is considered as the ultimate evaluation of a networked society 

17. An NS would emerge as the Central theme of llvlng wlth the societies trade 

economy, occupation, development, education culture and leisure all centred 

around networking. 

CONCLUSION 

Computer network~ng IS perhaps one of the most rmportant m~lestones In 

the rnnovat~ve creations using lnformat~on Technology (IT) and an even blgger 

phenomenon IS the lnternet lnternet has brought computer network~ng to an 

unprecedented frontler and can be described as the biggest IT event In computer 

and commun~cat~on technology In sclenllfic and research communlly, Internet IS an 

essent~al and ~nd~spensable tool Through Internet, sc~ent~sts can yaln Instant 

access to the world's most advanced research facilities and discuss the~research 

problems w~th others worklng In the same fleld They may be benefitted most through 

proper use of lnternet fac~llt~es after gainlng basic Ideas about the lnternet its 

navlgat~onal tools and servlces available as dfscussed above Never before such 

freedom of thought and expression have been posslble for ordlnary and not so 

ord~nary people allke At thls moment ~t IS very d~ff~cult to comprehend the 

consequences of the newly formed Cyber or Networked Soc~e~ty

ESTABLISHMENT OF LOCAL AREA NETWORK AND INTERNET UNDER 

THE ARISNET: A CASE STUDY 

G. R. Maruthl Sankar 

Contra! Research Institute lor Dryland Agricullura (ICAR) 

Sanloshnagar, Hydembad - 500 059 

1. Establishmant of NICNET at CRlDA 

During 1994-95, ICAR has made it compulsory for all ~nstitutes to establish 

Nal~onal Informatics Centre's Network (NICNET) for E-mail transmission through a 

MODEM and a dial-up telephone through Public Swltched Telephone Network (PSTN) 

connected to a Computer. Accordingly, CRlDA has established its NICNET services. 

Tlie services included transmission and downloading of E-mail messages through a 

low speed Multi-Tech MODEM and a PSTN through National :nformatics Centre (NIC), 

Hyderabad and further linkage to NIC, New Delhi through the Indian Satellite. The 

transmission of text was usually in the form of ASCII files through the PROCOMM 

software used for communication after getting connected to the VAX system at NIC, 

New Delhi. The protocol that was provided by NIC for all ICAR institutes was that of 

Simple Mail Transfer Protocol (SMTP) using which exchange of simple electronic mails 

can be exchanged. CRlDA has been provided with an E-mail address through X-400 

services of NIC, New Delhi as CRIDA@ X400. NICGW. NIC. IN for using the SMTP 

for exchange of information. Transmission of either non-ASCII text, graphics I images 

or use of any advanced software (Windows based) including the data except binary 

attachment was not possible due to the limitations of the PROCOMM software and also 

the protocol that was provided to the ICAR institutes. Further, the network was slow and 

problematic due lo the low speed of MODEM (1.2 Kilo bauds per second) being set by 

the NIC, New Delhi for all ICAR inst~tutes and the transmission errors in the satellite 

communication through the unreliable PSTN, apart from the problems In functioning of 

a telephone linkage. In spite of the different problems, messages have been transmitted 

and received periodically. 

2. Establishment of ARISNET at CRlDA 

During 1995-96, ICAR has made it mandatory for establishment of Agricultural 

Research Information System Network (ARISNET) at all ICAR institutes and augment 

the services of NICNET for exchange of agricultural research information, data and 

reports and various other kinds of information through the network. ICAR has supplied 

different hardware and software to all institutes for ARISNET establishment. Under 

ARIS.NET program, each institute was asked to establish a Local Area Network (LAN) 

through any of the three types of cabling viz., BNC, UTP or Fiber Optic cabling that 

suits the institute depending on the location, size and other requirements of the 

institute.

Accordingly. CRIDA has established its Local Area Network (LAN) under 

ARISNET during 1996-97. The network cabling for different rooms (54 nodal points) 

was done by the Electronics corporation of India Limited (ECIL), Hyderabad. The 

cabling has been done with the features of STAR Topology i.e., the Untwisted pair 

(UTP CAT-5) cables are connected from the ARISNET Server room to the different 

rooms through three 16-port HUBS (2 Bee-Line and 1 D-Link Hubs) which are located 

at three different places in the institute. CRIDA has been prov~ded with a SUN- 

SPARC UNlX Server (ICIM-Fujitsu make) and a Meteor LAN Server (HCL-HP make). 

While the UNlX server is a 8-node capacity Server, the LAN Setver is a 32-node 

capacity Server. While the UNlX Server was installed by ICIM-Fuj~tsu. Hyderabad the 

LAN Server and the three Workstations provided by ICAR have been itistalled by the 

HCL-HP, Secunderabad. The existtng NICNET has been merged w~th ARISNET. The 

NIC, Hyderabad has installed a htgh-speed Motorola MODEM (with a speed of 19.2 

Kilo bauds per second) for transmission and downloading of E-mail and other type of 

files and has connected it to the ARlS Workstation-l through a telephone cable under 

PSTN. Apart from the three ARlS Workstations provided by ICAR, 9 computers 

(nodes) from different rooms have been connected to the LAN Server The equ~pment 

supplied by ICAR are thus being used for day-to-day work with different software like 

Microsoft Office (WORD, EXCEL. POWERPOINT and ACCESS), Microsoft Visual C++ 

and other licensed software of the inst~tute. 

3. Establishment of VSATI Earth Station at CRIDA 

In view of advancements in computer hardware and software, and 

improvements in the Satellite communication and a revolution in the Information 

Technology all over the world during the last two years, ICAR has procured the latest 

Ku-Band Very Small Aperture Terminal (VSAT) from NIC. New Delhi and prov~ded them 

to a few selected institutes. The VSATs procured by ICAR are Frequency Time Division 

Multiple Access (FTDMA) VSATs, which have a very high downioad~ng and 

transmission speeds viz., 32 Kilo bauds per second (for transmission) and 256 Ktlo 

bauds per second (for downloading). They are very small, compact, less problemalic, 

less costly and highly efficient, easy to handle and have high speeds in cornmunical~on. 

They have many advantages when compared to the existing C-Band and S-Band 

VSATS of NIC in all features for satellite communication. CRlDA has been provided 

with a Ku-Band FTDMA VSAT. The Earth Station of CRIDA was developed and the 

VSAT has been successfully installed. The VSAT Earth Station of the Institute in 

Hyderabad is linked to the Master Earth Station of NIC at New Delhi through the Indian 

Satellite and will be catching signals uninterruptedly w~thout any error and are ut~lised 

for further processing. The VSAT has two units viz., an Out-Door Unit (ODU) and an In- 

Door Unit (IDU). The NIC has connected the ARlS Workstation - I to the IDU through 

UTP CAT-5 Cable. The IDU in turn is connected to the ODU of the VSAT Earth Statlon 

Ulrough enor-free UTP cables. The NIC, New Delhi has provided two dedicated IPaddresses 

(164.100.255.13 and 164.100.255.14) to the institute viz., one to the VSAT

(164.100.255.13) and the other to the ARlS Workstation - 1 (164.100.255.14). This is a 

statutory requirement for provision of INTERNET to an user by linkage to the Indian 

Satellite through a VSAT for direct communication with mill~ons of users on the World 

Wide Web (WWW). The ARIS workstation - I has been configured with the 

Transmission Control Protocol I Internet Protocol (TCP I IP) and the INTERNET has 

been provided to CRIDA by NIC, New Delhi. This Workstation has WINDOWS-95 as 

the restding Operating System (0s) and Net Scape Navigator Gold (3.1 Version) for 

browslng different Web sites on the INTERNET. Thus CRIDA has been provided with 

INTERNET facility for accessmg and browsing the WWW and downloading all relevant 

rnformation for furiher advancement in dryland research. Ever since the FTDMA Ku- 

Band VSAT has been rnstalled and INTERNET being provided to CRIDA, Scientists at 

the institute are making an efficient use of the INTERNET facility for direct transmission 

and downloading of E-mails, text and data files, graphics and images, browsing the 

W and visiting different Web sites for obtaining relevant information The 

information is oblained by vlsiting different Hyper Text Transmission Protocol (HTTP) 

addresses and making use of powerful search engines like YAHOO, ACTA VISTA. 

WEB CRAWLER, NET SEARCH and others that are available in the INTERNET. Most 

of Web ales can also be reached and the relevant Information that 1s requlred can be 

downloaded directly through the Hyper Text Marker Language (HTML) and JAVA 

software with proper protocols that are ava~lable in INTERNET. The NIC has provided a 

dedtcated INTERNET address viz.. CRIDA@AP. NIC IN to the institute for interaction 

with mtlllons of users on the INTERNET. The institute has been provided with a facillty 

for interacting with the Post Office Protocol (POP3) Sewer of NIC for exchange of mails 

through INTERNET drrectly. It is observed that the E-mails are transmitted and received 

with out any technical problem and in a quick lime through the INTERNET unlike the 

erstwhile PSTN through a Dial-up and a low speed MODEM. Apart from the Netscape 

Navigator Gold, Eudora Light and Alexa software are also used for exchanging E-mall 

and other information through POP3 facility provided by NIC, New Delhi. 

4. Establishment of INTRANET and INTERNET through LAN 

The ICAR has provided Novell Netware Version 4.10 which does not have 

INTRANET and INTERNET facililies. Hence it is not possible to get INTERNET facility 

for all nodes in the LAN through the existing Novell Netware software (Version 4.10) 

without dedicated IP-addresses. The ultimate requirement of establishment of 

INTRANET and accession of INTERNET on different nodes of users in CRIDA has 

been established by installing a Windows-NT server as an INTERNET sewer for 

different users through LAN. The users are able to browse INTERNET through PROXY 

server software and getting connected to the Windows-NT server. 25 Pentium systems 

localed In different rooms have been linked to the UNlX and Wrndows-NT servers for E- 

mail and INTERNET respectively. A dedicated Switch and 3 Hubs are used for 

connecting the users lo the servers. The UNlX and Windows-T servers are connected 

to the FTDMA Ku-Band VSAT for satellite communication and INTERNET browsing

The NIC has recently improved the bandwidth of VSAT and many users are able to 

access E-mail and INTERNET with out any difficulty. CRlDA has been making the best 

use of the INTERNET facility for research and development in different acttv~ttes, and 

thus making full use of the hardware and software. 

5. Role of VSAT In satellite communication 

Reliance on traditional ways of doing buslness like personal meetings, signed 

papers, and communication through normal terrestrial (telephone) lines is fast 

being replaced by wireless technologies like the VSATs 

About 6000 VSATs have been tnstalled in the country from 1995 onwards 

VSAT is a dish antenna along with integrated untts ~nstalled between 2 or more 

user locations They relay communicat~on signals between 2 locations through a 

satellite. They are suitable and ideal alternative to terrestrial communication 

Ilnes. Like terrestrial Ilnes, VSATs also rely on pipes which are Invisible in the 

sky which allow information to flow back and forth 

VSATs allow establishment of dependable links to sites where conventional 

telecom infrastructure is poor or non-existent Thts is useful for organizat~ons 

whtch have operations In reniote areas. They can easily be setup even in 

remote areas owing to their compact size, ruggedness and ease of tnstallation 

VSATs offer cheaper and cost effective means lo communicate as compared to 

land I~nes. The cost of a VSAT operation IS dlstance independent 

VSATs transmlt high volumes of voice, data and video any where In the country 

and also in the entire world. Corporate8 and different organizat~ons are trylng to 

march further by deploy~ng VSATs for commun~cation In India there are at least 

8 VSAT service providers competing in the market VSAT termtnal consists of 3 

elements A dish shaped antenna ranging In slze from 1.2 m - 3.8 m Outdoor 

unit mounted on the antenna for signal reception and transm1ssion7 Indoor un~t 

which connects to computer, telephone and customer equipment 

VSATs help companies in avoiding long delays involved In deployment of 

conventional teased Lines provided by DOT 

VSAT terminal transmits a radio signal to satellite. Radio s~gnal carries data, 

voice or images The satellite has a transponder whtch recetves the signal, 

amplifies it and sends it back to the receiver 

VSAT terminal operates In conjunctton with a large aperture hub earth station. 

This hub is installed and operated by a VSAT service provtder The hub directs 

the signals to and fro between satellite & communicating VSATs besides 

managing data transmission between them Advantages of VSATs

Independent of terrestrial infrastructure : Leased line networks from DOT do not 

normally service locations other than major cities and also line availability issues 

necessitates a lead time of 6 to 8 months. VSATs are deployed irrespective of 

these problems 

Distance independent costs: Cost of VSAT network and cost of data 

transmission are independent of distances and country specific tariff. 

Operational costs are lower as compared to leased lines 

High reliability : VSATs offer 99.5 % uptime when compared to at best 95 % 

offered by terrestrial lines due to very few or negligible polnt failures. They offer 

cross border connecl~vily as well. They are also useful for business houses that 

operate globally 

Easy scalability : Wlth ava~lable network, new sites can be commissioned rapidly 

with relatcvely l~ttle effort. Increased requirements of voice, data or video 

transmission from existing sites can also be met comfortably, with out a delay, 

from a central management system 

VSATs offer a rooftop to rooftop connectivity. Terrestrial back haul lines are not 

required. Thus there will no problems like in land lines 

Organizations that matched their network needs to right VSAT provider infer that 

VSAT services deliver connectivity that conventional network solutions cannot 

just match 

VSATs can be used across industries : VSATs provide cost effective solutions 

and meet all communication needs ranging from on line banking, ATMs, 

manufacluring, movement of relocation of orders to factories, online 

reservations on airlines, railways, hotels etc., These are also used in courier 

companies, RBD, financial institutions, publishing houses, television channels, 

stock broking, heavy engineering, consumer durables etc., 

Many organizations like Pepsi, Compaq, Citibank, Hong Kong Bank, Unilevers, 

Mahindra Ford, Procter and Gamble, Kelloggs, Nicholas Piramal and others 

have reaped the benefits of installing VSATs in their respective industries. 

Benefits have ranged from shorter order processing items, fewer stock outs, 

more control to savings in their operational costs Will VSATs save money 

* Yes. Voice communication is 75 minutes per site per day. Each site sends on an 

average 30 A4 sized faxes per day. Data transfer is 2 MB per site per day. 

Working days per year =3D 300 

If a company goes for a 9.6 Kbps link, using DAMA technology with a cost of 

11.5 lakhs per VSAT. The total capital investment of 46 lakhs is amortized over

5 years. The AMC is at least 10 % of capital cost and license fees to 

government are Rs.55.100 I- per VSAT 

' DOT charges Rs.43- per minute for voice, fax and data communicalion 

whereas VSAT service provider has offered a rate of rate of Rs.201- per minute 

for dial-up connection (V-Dial. Dama service from Telstra V-Comm) 

Not taking depreciation into account, a company would save Rs.28.90,0001- 

(42%) every year of its annual communication bill. ARer providing deprecialion, 

pay back period for capital investment would be 2 to 3 years 

Invisible savings like guaranteed uptimes (99.5 %) and greater connectivity. 

Better voice quality, more reliable faxes and data transfers and options of 

teleconferenc~ng, E-mails wh~ch reduce the need for repeated communication 

Better service & commercial terms offered by service provider lower the unit 

cost for higher usage. Videoconferencing could easily reduce cost of travel for 

review meetings, training programmes and annual planning processes 

Faster flow of critical communication (stock outs, dispatches, production 

schedules) would ensure an increase in business 

* For organizations which operate multiple locations or have higher 

communication needs, savings in operating expenses will be incredibly higher. 

For locations which need only tlata communicstions, the TDMA VSAT 

technology would serve the process at only 40% of the cost of DAMA VSAT or 

less, thereby ensuring that break even point is reached even earlier. How lo 

decide on a service provider 7 

Look for a service and a solutions approach. Reject mere equipment vendors 

Be sensitive to transparency in billing systems and itemwise location-wise 

billing. Some service providers typically operate by quoting low prices. They 

would make their money in annual service charges all at customer's expnee 

Look for a one stop , shop. . A service provider who performs activities starting 

from consultancy to network design, equipment supply, network implementatio" 

and even network management. You are better focusing on your core strengths, 

not running after an area-that you may no1 have expertise 

Ask for performance guarantees & other customer-friendly features such as 24 

hour help lines, trained man power, previous records etc., Look for a service 

provider who is moving with technology, with world wide trends and who could 

be your long term partner. Price is not directly related to efficiency. Lowest 

bidder in price may be the lowest in service too

PUTTING EDUCATION ONLINE : A CASE STUDY 

A. R. Thakur 

Bioinformetics Centre 

Deparlmenf of Biophysics 

Molecular B~ology and Genetics and Computer Centre 

Calcutta University 

Information Technology is rapidly becoming the all encompassing engine of 

development. This development is fuelled by an exponential growth in computing 

power, as f~rst observed by Intel co-founder Gordon Moore; microchips double in power 

and halve in price roughly every 18 months. Along with this, the second and equally 

important component which is pushing the information revolution is the rapid conceptual 

and technical development in the field of communication. A combined effect of 

development in these two areas, which has in effect become the third component 

pushing the information revolution is the concept of distributed computing. The idea to 

enable computers to work with documents stored in other computers gradually 

culminated into what is called W or World Wide Web of the Internet. 

The number of computers serving as hosts on the lnternent has exponentially 

grown to about 50 million all over the world and the number is increasing everyday. The 

internet wave has reached our shore late and only during the last 3 years has it really 

caught on. Initially it has been pushed forward through the combined efforts of ERNET, 

VSNL, DOT and NIC. This distributed networked availability of information has 

progressed much beyond transfer of Electronic mail or browsing information of the web 

site-the web surfing. 

lnformation Technology, at the threshold of twenty first century, is the most 

important tool that will form the principal component of all our economic/social activities 

including education. It may no longer be a fashionable proposition to debate whether 

harnessing this component for development is desirable, we may have reached a stage 

wherein it is imperative that we do so. Questions may be asked whether it is affordable 

and the answer is a simple yes. 

A major impediment in this technological revolution has been a few deeply 

ingrained misconceptions. These are: 

One has to be a mathemafical wizard to use computers 

Actually. 95% of the computer users are people who hardly know anything 

about even programming. W~thln the last decade and a half, advent of user friendly 

software for different types of works has made it possible to work with computers a 

simple task for any literate person. The technology to be handled is no more

complicated Ulan typing on keyboard or moving the cursor with the help of a 'mouse' to 

which one can easily become accustomed. It is no accident that Bill Gates is the richest 

person on Earth with an estimated income of $500 per second. The revolution that he 

initiated was to make the softwares user friendly to the extent that it made people shed 

their inhibition and start accepting PCs as part of their daily life. 

This is needed only for those involved in Science and Technology 

Any information that may be needed in any area which is now part of this 

process is available. I shall briefly narrate an incident to illustrate this point. Recently we 

were in a training session on Internet with teachers of Kldderpore college, mainly from 

faculty of arts. There was one request for anthology of Urdu poetry, and a site could be 

found wilh poems given in Urdu script. The second request was for a list of works 

available on Bhartendu Harischandra at lndra Office Library. Yes, we had to struggle a 

bit to get these but ultimately the information could be retrieved 

This is not an affordable technology in developing economies like ours 

I would like to submit that once we start thinking of quality teachlng which might 

determine the rate of our economic growth, larger sections of the society can be 

reaches properly only on adoption of these technologies. Distance Learning Education 

has now taken a new dimension in that it no longer fulfills the necessity of reaching out 

to the underprivileged and underachievers; today Distance Learning Education is 

synonymous with extending educational opportunities to those who have already 

become professional and would like to enture into new areas. Thus this should cover 

course curricula meant for reskilling people wilh a fair amounl of competency who have 

limitation of moving into a fixed educational environment for a specific stipulated period. 

It has been suggested that the education system in West Bengal has not kept 

pace with the more developed regions. One would like to contest the data since one 

does see a large number of students from West Bengal mannlng the various National 

Institutes in numbers in larger proportion than the relative population strength of the 

State. However that does not mean that one can afford to be complacent. In fact, that 

immediately suggests opening of new disciplines which is bound to attract the more 

adventurous students, who are not afraid to cross the boundary in order to gain 

knowledge. 

Java and other interactive technologies brlng new possibilities for developing 

content on the web. What does th~s capability mean for information dissemination and 

communication The capabilities of interactive technologies can now be used to 

effectively support communication amongst users. The significance and role of 

interactive learning is to be used in providing an environment for indrvidual and 

collab~rative work both within the University Department and externally. As intranet can

provide seemless access to a variety of information resources, this may be used to 

broadcast an interactive structured course work on a particular subject over the network 

which we might call Tele teaching. 

This will involve: 

i) Multimedia cooperative content creation. Every teacher creates hidher own 

course based on modular collection of semi-independent units (e.g. textual 

explanations; problems pictures; applets; videoclips of demo's). 

ii) 

iii) 

Database lo store 8uch ramourca units 

Teachers' interface for different assembly of a course by 'drag and drop' which 

will involve - 1000 html pages; -800 pictures (stored as gif files) - 50 Java 

applets; - 300 homework problems; - 10 interactively corrected multiple choice 

practice tests with solutions; - 15 separate questions 

How Is thls going to work 

Students' computers to have Browser with frame capabilities: 

Top frame for navigation: navigation button 

Selection of chapters and Topic via pulldown menus 

Checking own current progress 

Send E-mail to teachers 

Enter dedicated Problem Queries section 

Learn about the System (System Tour guide) 

Homework engine to have: 

lndivldualized problems: same text different data for each student 

Immediate feedback - in many problems; hints to be tailored to incorrect 

answers. 

The entire set of homework problems can be createdlmodified by the instructor 

only through the use of browser. 

Instructor Tools: 

View Table of Contents (ToC) of the course 

Copy ToC from another class 

Edit ToC according to the teachers choicelsludents' level 

EdiVintroduce homework problem 

Course admlnistration: Register new studentddrop existing studentslchange 

due dates for homework/assi$n markdassign system's e-mail recipients.

Objective of the proposed project: 

Calcutta University has nearly 230 aff~liated undergraduate colleges. Recently a 

course on Environment has been introduced as a compulsory paper at the 

Undergraduate level. There is a strong need for an interactive course material to be 

made accessible to the teachers and students. A sophisticated computer network 

infrastructure involving optical fiber backbone connecl~ng the different bulldings in the 

Rashbehari Prangan connecting different Departments is in place. From Alipur we have 

established a dial in PSTN connection between the two Routers. The connectivity at 9.6 

kbps gives 200 ms connect time. whereas for the I-NET X.25 leased llne it is 400-600 

ms. The VSAT (641128 kbps) is being used for Internet browsing by about 100 nodes 

spread all over the four campuses. We are in a pos~tion to offer the undergraduate 

Colleges connectivity. A kit consisting of a Router, a hub and a modem is now ready 

with which the connectivity can be tested from any collegeiinst~tut~on using PSTN line 

This project envisage development of on-ltne courses based on the Syllabus for I) 

Environmental Studies ii) Computer Science (both at Pass and Honours level) ill) 

Molecular Biology iv) Electronics Science etc, at the Undergraduate level 

Recent development 

lnd~a was one of the fist few countries that had taken glant step In 1986 and 

establ~shed a nat~onw~de B~olnformatlon System of lndla A D~stributed lnformat~on 

Network under the aelgies of Department of Biotechnology was establ~shed in varlous 

Un~versit~es and research lnstrtutes As a consequence of th~s ~nit~atlve computer 

llteracy and awareness amongst 910-sclentlsts grew Large number of publlc doma~n 

databases w~th regular updates, In the area of molecular b~ology and genetlc 

englneerlng became ava~lable to sc~ent~sts In lndta Computer hardware to carry out 

s~mple data analys~s and modell~ng has also become ava~lable and network~ng of 

computers IS now provlded on a regular bass through NICNET and ERNET establ~shed 

V-SAT l~nkage Calcutta Umverslty has already establ~shed its own Network the need 

of the present 1s development of human resources and for thls network can be 

harnessed to teach more effect~vely In far flung Inst~tut~ons by mak~ng ava~lable to them 

course rnaterlal by teleteach~ng 

International Status 

It is now global\y felt that the fruits of the Informatton retrieval, processing and 

analysis should be put in a networked environment so that greater benefit can accrue to 

the society. Even a market driven economy of the western world understands the 

necessity of dissemination of knowledge over the Net so that browsing or surfing the 

Net is no longer for pleasure but necessary for gathering vital teaching matertal. 

Michigan State University has already set up a web site based Lecture Course on 

Physics.

Methodology to be adopted 

The campus at Ballygunge Circular Road has an extensive LAN maintained with 

UTP cabling. Similarly the Central Libray Complex at College Street campus also has 

an operative LAN. These are connected via the I-NET could using (X.25 and X.28 

PADS). The four-port Router at Rashbehari Prangan can provide Dial-in service via the 

I-NET could as well. Undergraduate Colleges could be tested as a node where 

teleteaching material as well as Offline database could be made available over the 

Intranet. 

Due lo ever-increasing importance of the Web as a distribution channel and 

communications vehicle, organizations are racing to meet the demand for media-rich 

content on the Internet and the~r intranets. Education and training on demand is just 

one example of the innovative use of media streaming. WebFORCE MediaBase 

streams audio and video to the desktop, bridging the gap between those learning and 

those teaching through a Web environment. Video lessons streamed to the desktop 

allow for education on demand, including both live and delayed access to a lesson. 

Real-t~me Webcast~ng allows students the flexibil~ty to see lectures live from an off-site 

location, through the familiar-interface of a Web browser. Course on demand allow 

even greater flexibility by archiving and cataloging lessons or sieeches that can be 

searched lor an delivered as needed with a keyword or topic search. 

Medis delivery in for /he educational institutions like colleges or schools 

Computer based Training applicalions. In fhe educational sphere the media 

server can be used for: 

Interactive computer based training 

Multimedia centres established by universities to train students in new technologies 

Dtstance learning, live recording and broadcast of classroom lectures over campus or 

external networks and the storage and cataloguing of these lectures for future viewing. 

Archiving and cataloguing of the various media assets at different departments at the 

educational institutions. 

Repurposing media assets for on-line education. 

Work to identify the applfcation requirements must be made and for that certain key 

questions should be answered: 

What is client platform 

What is the underlying network (topology and protocol) for distribution of the media 

What are Ihe video quality, bandwidth and format requirements How many clients are 

being served concurrently is there a need for multicasting How much content will the 

customer be receiving 

Are media server, storage content and network management important

What is the media format used (MPEGI. MPEGZ, H.263) 

Is live encoding and broadcast required or desired 

Is 'server-up-time' a critical issue 

Before a media server solution is decided it is important to understand the type of 

application that is being proposed. Some of the iniportant information one must have: 

Display device for the media; Windows 95; Windows NT. Irix, Solaris; AIX; Network 

Computers Transport protocol IP or ATM 

Network topology from the server to the cl~ent 

The video format that is bebng used currently WebFORCE MediaBase supports 

MPEGI, MPEG2 and H.263 formats V~deo can be streamed using natlve IP protocols 

(UDP) or AAL5 for pure ATM netowrk. 

Number of concurrent streams that IS being planned along with hours of video that need 

to be stored and streamed. 

Hardware or software decode for the client side should be identified. 

WebFORCE configuration for 0200 with CPU (4R10K) with 256 ME RAM 60 hrs 

of Videocontent may be stored using 100 streams This would need additional disk of 

56GB. The Price is - $20000.00. 

Once this is standardized, interactive coursewares developed to cover some of 

the specialized subjects like Environment, Microbiology, Molecular Biology, Electronics, 

Computer Science could be kept for access by both students and the teachers of the 

Undergraduate Colleges. We believe the interactive progamme w~ll ensure that the 

teaches wouldn't feel theatened as in a top down approach 

How is the project to be integrated to the educational system 

The proposed West Bengal net of universities 

The idea of this network gets its birth from the immediate need to establish 

communication between the major educational institutes in West Bengal for data, email, 

and remote education program. Right now a lot of these institute e.g IIM. ISI, Calcutta 

University, S. N. Bose Inst~tute. etc, have lheir own LAN and access to the W on 

leased lines. All these centres of education are to be broughl under one platform using 

a resilient, upgradable, scaleabie h~gh bandwidth backbone network. At a later stage 

this nelwork can afso be used for private Voice traffic, which will help project 

investment. The various affiliate colleges under the various universities should be able 

to dial into the backbone for sharing of resources. This intranet should also be used for 

internet access from one or multiple gateways in the network in such a way that there is 

hgh avaiiability if internet access to all users in the network. The currenl network at

Calcutta University connecting the various campus can be model 8 used as building 

blocks in the design and construction of this wmplicaled intranet. 

The current cal university network 

The Calcutta University network can be the basis of our proposed network. In 

the light of the above we need to look into the design of this IBM switch 8 router based 

intranet. The campuses in this intranet are: 

Rajabazar Campus 

Bailygunge Campus 

Alipore Campus 

College Street Campus 

The various branches are connected via the INET X.25 network. Alipore has a 

d~al up connectivity to Ra]ebazar. For internet access right now the Rajabazar campus 

acts as the gatrway to ERNET @ 64 kbps, through VSAT. 

Concerns in the network: 

The network la not secured. It does not have a proper proxylfirewall which might 

lead to data hacklng and Intentional instrusion into this network. 

Bottlenecks In band with can be a cause of concern as the network grows with 

more colleges dlallng thls network. The backbone is presently only at 9.6 kbps at which 

only emaii transfer can happen, smoothly Mission critical application and multimedia 

applications e.g. remote teaching program will definitely needs much higher bandwith. 

Video conferenclng too requires much higher bandwidth. 

For internet access the network is dependent on ERNET. But again to cater to 

the high number of Internet users in this huge network a fat pipe to VSNL at 512 kbps 

or more is ideal. 

How can we design thls network 

The final network as we envisage is to encompass the following Educational 

Institutes 

Calcutta University - 4 locations 

Jadavpur University - 2 locations 

Vidyasegar Univenity - Midnapore 

Rabindra Bharati Univenity - Calcutta 

Viswa Bhanti Univenity - Santiniketan

North Benal University - Siliuri 

Burdwan University - Burdwan 

Kalyani University - Kalyani 

BE College - Howrah (Calcutta) 

IS1 Calcutta 

IIM Calcutta 

SN Bose Institute Clacutta 

Saha Institute Calcutta 

IACS Calcutta 

Bose Institute 

All the affiliate colleges under the universities (> 400) 

Fisheries University - Calcutta 

IIT - Kharagpur 

With all these inst~tutlons brought w~thin the lim~ts of a single network they need 

adequate bandwidth for effective data commun~cation and also to protect investment, 

the network should be designed only after through brainstromlng & careful and 

meticulous study of requirementslapplications and the various options available in 

terms of the WAN media. The design is also somewhat dependent on the extent of 

security and network monitoring required 

To start with we can break the network into phases and look tnto the varlous 

media available at this stage. The network des~gned today should be able to 

accommodate new technologies of tomorrow, 

The various media: 

To start with one can continue with INET but keeping in m~nd that this would 

include running critical applications l~ke remote teaching programme, library on the net, 

File download, mult~medi application as well as video conferencing and voice at a later 

stage a higher leased bandwidth is definitely required and this is what holds the key to 

the effectiveness 8 usabil~ty of the network. 

The various alternates for having high bandw~dth w~thin the intranet are: 

641128 K DOT Terrestrial leased lines 

641128 K VSAT Priority Assigned multiple Access links 

Demand Assigned Multiple access VSAT links 

ISDN Basic Reserved Interface links from DOT (2B+D) 

For access to internet a good option is to have onelmult~ple leased links to 

VSNL through multiple gateways within Ihe network These links can be establ~shed 

through DOT leased IinksllSDN.

A Tiered Network 

To ensure a properly planned network that can be administered with ease we need to 

tier the network as: 

The network can be constructed In line with the internet which has a backbone 

on OSPF and Access network and is a IP network in total~ty. 

A router based IP backbone connecting the nodal universities 

A strong, resilient 8 redundant backbone hold the key to the functionality & 

scalibility of the entire network. Within Calcutta 8 its suburbs we can have the 

backbone on ISDN dial up That can give us upto 128 kbps of bandwidth. Between 

Calcutta 8 distant locations it is ideal to have 641128 k bandwidth using VSATIDOT 

leased links. VSAT w~ll def~nitely be beler in terms of reliability over DOT leased 

circuits. 

Consultat~on w~th DOTNSNL necessary 

A remote access network 

To start w~th the various affiliate colleges can dial up into the nodal points 

through RAS (rernote access server) and can gel into the nelwork It is very important 

that colleges should get a committed high bandwidth on demand. Here the two options 

are PSTN d~al upllSDN dial upi9.6 k leased164 kbps leased. The networking 

equ~pnlents should however be able to support 64 kbps in future. 

Consultation with DOT essential 

Internet access 

This intranet should also be available to the WWW and the users of this 

University network should have unhindered access to the internet. 

To ensure this there should be preferably two 64k leased links to VSNL from 

two nodal centres. 

The reason for having 2 links instead of I is to distribute the internet traffic 

through 2 points and hence reduce bandwidth clogging at a single gateway. 

Consultation with VSNL necessary.

Network security and monitoring 

A network of this stretch and magnitude needs utmost security for seamless and 

smooth functioning. Hence 11 should have multiple Proxy servers of high processing 

power to 

1. Ensure that the network is hidden from the interne! and hence secured from 

being hacked by firewalling mechanisms. 

2. The overheads on the backbone are reduced and hence network becomes 

faster due to proxy caching. 

Again to prevent downt~me of the network by early identiflcat~on of faults in the 

network. The network needs to be managed uslng a central SNMP Management 

Station. 

Local LAN at each site 

Finally one very important part of the network is the LAN at each slte. In order to 

effectively use the backbone the LAN at each site should be state of the art. All the 

sites should preferably have structured cabllng with a switched environment and F~bre 

at the backbone and UTP at desktops. The campus LANs can well be bull1 around ATM 

switches. 

Servers 

In add~tion to the proxy servers there should be DNS servers, mall servers, 

terminal sewers, d~g~tal l~brary server and web servers at one or more nodal site 8 

replication if poss~ble at other sites 

This, thus forms the basis of the proposed intranet However detalled studles on. 

1. Load Calculation 

2. Degree of redundancy 

3. The type of routing protocol to be sued 

4. The extent of security 

5. The type of management 

6. IP planning etc. 

is required to finally arrlve to the ult~mate design. 

In this regard we are looking towards the deslgn of a network that is 

Technically flawless 

Commercially viable 

Scalable i% Upgradable 

Should be able to grow

How do we go about building it 

The Calcutta University Network needs to be augmented. 

We have been given one Cluster C-series of IP address (256). Current scheme 

has exhausted the list. We are to put in Proxy server. We have downloaded 

LlNUW~ndows NT based demo version. These are to be ported on the 

Compaq Windows NT servers for which we have already placed order. 

We need to put a Firewall for security. Price Rs. 2. lakhs 

The RAM of the PC Servers and the machines currently used for internet 

access have to be increased. For this order has already been placed. 

The 9.6 kbps X.25 leased line INET needs to be upgraded to 64 kbps. 

A CD-Juke box has to be put in conjunction with the CD-NET at College Street 

so that we can start the Off-line database service. Price DM 28,000.00 (NSM, 

Germany) 

Web Server Sun Ultra Sparcll Rs. 10 lakhs 

Digital Library server: Origin 2000lRAID Rs. 20 lakhs 

Dial-in Server: Capable of 5 telephone lines on a hunting mode; Rs. 50000.00 

Budget Proposal by March 1999 

CD Juke Box 

Software for CD-NET 

Upgrade 9.6 kbps to 64 kbps 

Upgrade RAM 

Dial-in-Server 

ProxylFirewall Software 

5-telephone lines 

Laptop Computer 

Libsys 

Software for On-line Teach 

workstation for On-line 

ERNET 2 Mbps upgradation 

Total: 

What would we be getting 

1. Connectivity upgraded to 64 Kbps 

2. Off-line database with 150 Cds. 

3. 5-6 Colleges out of 100 colleges within Calcutta Telephone gets connected. 

4. A kit comprising of (1 Router; 1 Hub: 1 Modem; 3-4 Patch cords: 1 Laptop 

Computer) is kept ready for checking the connectivity with colleges and 

universities. 

5. Preparation taken for On-line teaching of courses from emerging areas.

EXISTING NETWORK A T CALCUTTA UNIVERSITY 

I

WEB SITE DESIGN 8 HOSTING 

Bikash Panda 

HIG-188, Kanen V~har, Bhubeneswar-75103 1 

The Internet's World Wtde Web is like the W~ld West. Anarchic, disorganised. 

exciting and with minimal standards. By now there are approximately about 48.00,000 

web sewers maintaining about 45 crores of web pages. Every corporate house, 

educational & research institute, small business concerns even indivlduat users expect 

to have a presence in the Web and moreover, every web site expects to attract as 

much visitors to browse their information contents. People visit the sites which are well 

organised, informative, easy to navigate, interesting content, good to look at and 

nevertheless, useful. This imposes a challenge on the web designers to have an edge 

There are no standards to define what a good site is. However, a consensus has 

emerged for the same. 

The language of World Wtde Web is HTML wh~ch stands for Hyper Text Markup 

Language. The word 'Markup" indicates that HTML is a formatting language and not a 

programming language. This concept makes the language easy lo learn 8 easy lo 

implement. Web pages are basically HTML documents whlch are inlerpreled by Web 

Browsers ltke Microsoft Internet Explorer or Netscape Navigator. A HTML docuri)ent 

IS an ASCll text flte that contam HTML tags 8 these tags decide how the web page 

looks like when browsed. Being ASCll files, you do not require any spec~allsed 

Compiler or Interpreter or IDE to wrtte or use them One can use the most cornmon 

Notepad or Wordpad or even DOS's own edit com or Unix's vi editor to write them. The 

HTML files have an extension of HTM or .HTML. 

The following section describes few commonly used HTML tags and other web 

development concerns. 

The HTML tags are special keyword wrltlen between < and > slgns An example 

of an HTML tag is There is no hard 8 fast rule in wr~ting the tags in Uppercase 

but it is advisable to use Uppercase letters so as to differentiate it from the text of the 

page. 

A typical web page may have the following contents Let us name the page rnyf1le.htrn 

 

 

Welwrne to CIFA, Bhubaneswar 

4iTML> 

Central Institute of Freshwater Aquaculture is situated in the outskirts of temple city of 

Bhubaneswar in Orissa. 

 

 

This page when viewed in your preferred browser would display a heading in the 

top of the screen as Welcome to CIFA, Bhubaneswar and the body of the browser 

would display the text 'Central lnstltute of Freshwater Aquaculture is situated in 

the outskirts of temple city of Bhubaneswar In Orissa.' Please note that when 

indicating the start 8 end of the tags, the end tag must have a I in them. You may find 

this used as 8 , 8 elc. 

In the browser window only the contents of - are shown. The 

tag contains information which are not shown in the browser but have other 

use like the Header information, author's name elc. 

The tag inside body displays the text as Headinl;. 

Example : 

lntroduction to CIFA would show 'lntroduction to CIFA" as lntroduction to 

CIFA 

Smaller Headings are possible with tags thru 

The following tags helps us in formatting the text. 

denotes the start of a new paragraph. 

tag puts a line break in the text 

For making the text Bold 

For making the text Italics 

XU> For making the text Underlined 

Adding Plctures to Web Pages: 

Pictures speak thousand words. Graphics makes a web site attractive.All 

pictures must be converted lo one of several digital formals, so you'll need a scanner 

and software (such as Adobe Photoshop) to manipulate the picture into the form you 

wish to display it in: the pictures don't appear there magically1 To get your pictures to 

display on a Web page, you must use certain HTML tags to "point to" the picture Rles 

that, like your HTML files, have been uploaded to a server. Where and how you place 

the tags deems how the art will be viewed by a particular user. 

Pictures can be saved in a variety of styles; the GIF format is the most 

commonly recognized by various browsers, and is thus most commonly used. .ihe 

JPEG format is also fairly common; it creates better quality photos, especially with 

scans. A program called GIF Converter Is also helpful; it converts files saved in the

Maantosh PlCT format to either a GIF or a JPEG, and allows you to edit the files 

Here is the most common tag used to find and place a picture on a Web page: 

provided. Third, you will need a private account on a Web server-a computer 

permanently connected to the Internet-so you can upload your files to it, and other 

people can see them. 

You also must be able to transfer your files to the server. For IBM and 

compatibles, use any FTP (File Transfer Protocol) client (there's a basic one built in to 

Windows 95); one of the easiest to use is Cute FTP. From there, you have the choice 

of a few different options for getting your pages up on the Web. 

Depending on your circumstances at school or at work, you may have to pay a 

fee to keep your pages on the Web; the rates will vary from provider to provider. 

University servers will sometimes upload student or faculty pages to their server for free 

or a minor fee; if you work in a company that allows you to use their server, that's 

another option. If neither of these are possible, you'll need an independent ISP (Internet 

Service Provider), price !he options, then upload the information to the provider so they 

can put it up for you. You will be paying a fee (most likely on a month-to-month basis) in 

this case. Fees could be flat, but many times they depend on how many people are 

accessing your site (called "hits"). The more hits, the more taxing it is on the server, 

and potentially, the more you'll pay. 

HTML Editors 

As one can imagine writing HTML tags for longer documents can be very 

dtff~cult 8 confusing. As on now there are hundreds of HTML editors which work as 

WYSlWUG (What You See Is What You Get) style, which helps you write good Web 

pages conveniently. The most popular ones are Mic-xofl Frontpage, Hotmetal's 

HotDog, Dream weaver etc. 

Web Design Considerations 

Here are few web development guides for making a good web site 

Set Objectives for the Web Site: 

Define the target audience clearly (Whom do you want to influence) 

Esttmate audience technology profile (eg bandwidth, type of browser etc) 

Perform audience needs analysis 

Be clear about your purpose (sales, service, education, research, entertainment) 

Define the scope of Content: 

* Do not Use unnecessary words 

Provide useful information on each page 

* Design for all browsers

Use Graphics Judiciously: 

Limit large images used for visual appeal only 

Keep the total size of graphics on a page less than 50K 

Limit the use of graphics bullets and lines 

Ensure good contrast between text 8 background colour or images 

Plan for easy Navigation: 

Give each page an appropriate title 

For long documents, provide return to Top or Hornepage links 

For large sites, provide a search engine or index pages 

Indicate the date of last update of the site 

Avoid use of frames 

Provide guided tours in appropriate situations 

Web design is more of an arl than programming. A good designed site can be the best 

medium one organisation can think of lo promote their objectives. 

About the author 

Bikash Panda is a BE(Electronics). MBA(Systems) and has Web development experience of 

more than 3 years in India 8 Abroad 

He can be coniacted at HIG.188, Kanan Vihar, Bhubaneswar-751031, Te1.91674-440702, 

Email : bikash@ma~lcily.cwn

MULTIMEDIA - a maglc mantra 

Jayaram Parida 

(MCS. Multimedia 6 Web Developer) 

NAVAGUNJAR 

Multimedia end Web Technology Lab 

9, Sweet Housing Complex 

Ganganagar, Bhubaneswar - 751 006 

Multimedia is a much used, over-used and abused term. Since the early 1990s 

multimedia has been hyped as a major revolution in computer technology and is hailed 

as part of "the next big thing". As with any bandwagon, there are many people looking 

at multimedia from different points of view. As we are considering multimedia from a 

Media Product~on viewpoint we need to define multimedia in terms that allow us to 

compare and contrast multimedia with other media products. 

As multimedia is so new there are riot any clear conventions about what is and is not 

multimedia but as a starting point we will work with the following def~nition: 

Multimedia is a really an adjective not a noun1 You can't really talk about multimedia full 

stop. You have to talk about a "multimedia something" We are talking about multimedia 

producis. These are media products with the following characteristics: 

They are delivered digitally. This usually means that some kind of computer is 

required to use the product. This may not be a conventional looking desktop computer 

(although it can be). It could be a Sega or Nintendo games console. It could be a settop 

decoder box or a CD Player. it might be a hand-held personal organiser or a mobile 

phone. The key that distinguishes digital technologies from the rest (analogue) is that 

large amounts of information can be stored, searched, displayed and manipulated with 

ease, Digital technology also makes it easier to allow the consumer to enter their own 

information and make there own choices- inleractivity. 

They use a range of audio-visual forms. Traditionally, information delivered via a 

computer has been text-based with perhaps some basic graphics. Multimedia products 

are based on the assumption that it is best to use the form most appropriate to the 

content. As computer technology has improved, it has become possible to display high 

qual~ty still images, v~deo and animation in addition to text and graphics. Whilst using 

these visual mediums it is also posslble to play high quality sound- music, voice-overs, 

sound effects etc. This allows the product designer to provide a much richer 

environment for the consumer. It is argued that this enhances their experience. 

They are interactive. Many traditional media forms are passive. The consumer can't 

decide what stories appear in a newspaper. They can't directly influence the narrative of

a N drama. They can't respond immediately to r radio adverl. Interactivity allows the 

consumer to influence the material that is king presented to them - to interact with it. 

The nature and amount of interaction varies tremendously. For example, a Ninlendo 

games console is highly interactive the whole experience hinges on the user's 

manipulation of the controls. Home Shopping may be less frantically interactive but still 

allows the consumer to respond directly to the content that is being displayed. 

Introduction- Still Images 

In this first main practical topic you will look at how the most basic elements of any 

multimedia product are constructed. The term "Still Images" covers a wide range of 

different parts of a multimedia production. It refers to any static graphics, photographs, 

design devices and even text sometimes. Sometimes you will start a screen from 

scratch on the computer but there is often a need to capture existing graph~c material 

such as a photograph or a logo into the computer so that you can work on it before 

including it in the flnal product. 

Capturing Still Images 

Capturing a still image means taking an existing Image and transferring it into the 

computer so that it can stored and used in digital form. The method you use depends 

on the form the existing image takes before you start. 01 course the image may not 

exist at all so you will need to do some photography first. If this the case then consider 

using a digital camera. This will cut out an intermediate stage. If you want high quality 

images from scratch then you can take conventional photographs and have them 

transferred on a Kodak Photo CD which can then be read by the computer. It is often 

the case however that you already have the image as a photograph or on a prrnted 

page. It this situation you use a flat-bed scanner d~rectly connected to a computer to 

capture the image. 

Scanning 

The flat bed scanner is used to capture existing still images that are in a form that will fit 

flat against the glass plate. This usually means paper but it doesn't have to be- you can 

scan fabrics, leaves, silver fotl etc. a3 a means of generating textures There Is a 

scanner in all the computer suites that you use. Using the scanner IS fairly straight 

fomard but like anything in multimedia it needs to be done carefully following these 

instructions exactly. 

Place your original artwork under the scanner cover, face down. Align the corner of the 

picture with the comer of the glass indicated by an arrow. This usually means putting 

the picture In upside down. Launch the application Adobe Photoshop. This program is 

probably in a folder called Applications but it could be anywhere on the d~sk. :f you can't 

find it use "Find File" from the Finder File menu The application icon is shown here.

PhotoShop is a popular, powerful program for creating and manipulating still images. 

You access the scanner by pulling down the File menu and holding the mouse down on 

Acquire. This displays a sub-menu that shows the name of the scanner software. This 

will vary depending on the make of scanner but is usually obvious. 

The scanner may have settings for adjusting parameters such as brightness and 

contrast. As a general principle, leave all these settings at their defaults. Scan the 

image first and then do all the correction afterwards in PhotoShop. PhotoShop gives far 

greater control over the image and if things go wrong you can always revert to the 

original scan and try again. Click the preview button. The scanner will quickly scan the 

original at low resolution, showing you a thumbnail view of the whole image. You will 

often want to scan only part of the image so use the mouse to click and drag a 

rectangle over the area of the image you want to scan. Click the scan button. The 

scanner will scan the parl of the image you have selected and the open a Photoshop 

window containing the scanned image. You can then modify it andlor save it as you 

wish. 

Dlgital Camera 

Digital Cameras are useful when the image you want doesn't exist. You can go out and 

shoot Images and then transfer them directly to the Computer without going through the 

traditional route of developing, printing and then scanning. The disadvantage of using a 

di~ltal camera (or at least a cheap digital camera) is the image quality. The quality is 

much lower than conventional photography. 

Comparison of techniques 

All three of the ways of capturing images discussed above have their advantages and 

disadvantages. In deciding which to use you should be aware of these: 

Scanning gives reasonable quality and is fairly quick provided the image exists in a 

form that can be put under a flatbed scanner. 

Digital cameras are quick and easy to use when you need to originate the image but 

the quality is only average and they are expensive. 

PhotoCD gives excellent quality and you don't have to bother with scanning but you 

have to wait for it to be processed and it can be expensive. 

Thia shows that there is no right or wrong way to capture images- you have to choose 

the best tool for the job.

Capturing Sound 

In the same way that you often start screens with a scanned image you will often need 

to start a soundtrack by capturing and storing some existing music or sound eifects on 

disk so that you can incorporate them in your production at the authoring stage. 

Existing sound recordings can exist a number of forms. The way you capture these into 

the computer varies according to the form the track takes. The easiest audio to capture 

is from conventional audio CDs. However, if your track is on audio cassette tape then 

you can still capture it quite easily. This will usually be the case if you have recorded 

your own track with volceoverslcommentary etc. Occasionally It may be necessary to 

capture the audio track of a video tape. This uses the same technique as required for 

audio tape so it is not covered in detail here. Once the audio track has been captured it 

can be edited to meet the requirements of your multimedia package. The resulting track 

can then be superimposed onto the visual material at the authoring stage 

Capturing from Audio CD 

If you need to capture a track from an audio CD then here's the procedure' 

1. Load the CD Into the CD drive of the computer. An icon represenltng the CD w~ll 

appear on the desktop. Don't bother double-clicking it- that isn't the way in1 

2. Locate and launch the application SoundEdit 16. This Is a general purpose 

sound capture and editing program. It is to sound what Photoshop is to images. 

Capturing Video 

Capturing Video is somehow a bit tedious process on the desktop computer. The video 

capture card is bit costly than a sound card. And also to capture a long duration video 

file takes more space. For example If we want to store 10 minute video data , then it 

requires 100-200MB of disk space to store the data on to the disk. 

Some good video capture cards are Miro DC-30, Bravadoo-2000, Truevision Targa pro 

and some low end capture cards are Video Blaster, etc. 

To Edit and capture video to the computer on a full frame full motion we require more 

video ram and also more RAM at least 32-84 MB(SD0 RAM). Adobe, Premier 5 is a 

best software for non-linear editing and ~pecial Effects. There are also so many 

software and editing system8 are available for broadcast quality production. They are 

SGI, AVID systems.

Fine I it's a separate topic that which require so many think to the spare, we should now 

move to combine all the Text, Picture. Sound, Video and to produce a complete 

CDROM . 

PREPARING SCREENS FOR INTERACTIVITY 

Introduction to Creating a Screen 

Once you have acquired all the images that you need you can then build them into a 

screen which can be then combined with other screens in an authoring package to 

produce the finished product. You will always use Adobe Photoshop to do this job. 

Photoshop is an extensive package that can be used for many other tasks as well. 

Rather than give you a general introduction to Photoshop this section allows you to 

work through the construction of an example screen. This is the quickest way to get 

results but you should take time to explore Photoshop and find out what else it can do. 

Having prepared our Text, Image, Audio, video we are now ready to import them into 

Macromedia Director in order to make our piece of interactive multimedia. 

Macromedla Director 

Director is an application which uses the metaphor of a film studio: There is a STAGE 

on which all the action comes together, a CAST, a SCORE which allows you lo 

orchestrate objects through time and a CONTROL PANEL which controls the action. 

There are also more computer-like tools for creating text, images, and other objects on 

the stage. Each feature is represented by a Window and each window can be open at 

the same time so you can work easily (provided you have a big enough screen) 

between the features. 

As the director of your own Movie (as the finished file format is called) you can 

orchestrale a number of already created objects (cast members) around the Stage. 

These objects could be Photoshop files, QuickTime movies, sound files or text files. 

You can layer these objects up in the Score so that they can play one in front of the 

other on the Stage. 

It Is the interacllv~ly ill Director that makes it really powerful -- you can programme the 

Score and indiv~dual Cast members and so control their behav~our by using Scripts 

written in Director's own programming language Lingo. Transparent interactive areas 

(buttons).

Multimedia Authoring Contents 

Importing the Cast 

The first stage is to import the prepared Pict screens. Select Import from the File menu. 

The dialogue box allows you select mom than one file at a time. You can choose to 

import the bitmap at its original colour depth or at the stage colour depth. You also have 

the choice of importing the Text, Audio, Video to the Director. 

The files will all appear in the Cast window. Now your Director movie should be 

interactive. 

Creating the Score 

The score is the most complicated part of Director. It consist8 of an ever expanding 

window that shows you channels horizontally and frames vertically. 

At the top left of the score there are control channels that let you adjust timing, create 

Colour changes; insert transition effects; and add sounds. You access these features 

by double-clicking in any frame in that channel. 

The best way of placing the cast members on to the score is to select them in the Cast 

window (by Shifl-clicking or choosing Select All from the Edit Menu). 

Adding lnteractivlty 

The next stage is to add buttons to the screens by putting an invisible box around each 

of the buttons we created on the Menu screen in Frame 1. For this we need to select 

the Tool palette from the Window Menu. Choose the empty rectangle and ensure that 

the no line option is clicked. 

They also appear as new cast members in the Cast window (as do the scripts). Double 

click on the button in the frame and the Cast Member Propert Window will appear. 

Click on scrip1 an type: go to frame 10 . Do the same for the other buttons 

The next stage is to put tnvisble redangles over the return to menu buttons In each of 

the other screens and write the script "go to frame 1". 

Now your Oiredor mo& should ba interactive.

Multimedia Authoring Content8 

Making a Projector 

At the moment the movie can only be played using Director. It is possible however to 

turn it into a Projector - a self-contained program which can be played without Director 

even king on the computer. 

NAVAGUNJAR 

Multimedia and Web Technology Lab, 

9, Sweet Housing Complex, 

Ganganagar, 

Bhubaneswar - 751 006 

Tel : 91-674-425310,427514 

Email : jayaramp@yahoo.com

MULTIMEDIA -on the Web 

Jayaram Parlda 

( MCS, Mulfimed~s 6 Web Developer ) 

NA VACUNJAR 

Mullimed~and Web Technology Lab 

9, Sweet Housing Complex 

Gangsnagar, Bhubaneswar - 751 006 

Multimedia is a technology which is have everywhere uses for making the 

thinks more attractive and more Interactive. Web Technology was dry without 

multimedia on 90s. When technology updated by putting graphics on the WebPages 

and later come to the animation. And finally now the revolution of real Audio and Real 

Video which plays a great role on the web and yet to be advanced for more realistic for 

the standard system and real application. Here is an detail overview of pulting 

Animation, Streaming Aud~o and Streaming video on the web for your web Page 

design. 

Getting lnto Motion -a Guide for Adding Animation lo Your Web Pages 

As a frequent Web traveler, you've probably encountered a number of pages 

that contain various animated objects--from bouncing logos to ads for speeding cars 

and bubbling aofl drinks. It used to be that a striking background image or a fancy rule 

line was all that differentiated the average Web page from one that was really cool. 

That, however, has all changed with the advent of animated GIFs, Java applets, and 

Web browsers that make it easy to host these new elements. If you're thinklng that 

you'll have to learn a new programming language, you can breathe a sigh of relief. 

Although we'll explore animation techniques that rely on Java, there are several ways 

you can spice up your pages without having to perform any programming. 

GIF Conrtructlon Set 

On the PC, the most popular program for creating animated GlFs is Errorl 

Bookmark not defined, from Alchemy M~ndworks. This easy-to-use, inexpensive 

shareware package supports image looping, interlaced GIF images, and transparency. 

It also features an Animation Wizard that will guide you through the process of selecting 

and preparing an animation sequence. 

Two other notable features in Construction Set are the "banner" and "transition" 

tools. The banner tool allows you to type in a text message, which is then turned lnto a 

scrolling GIF image. The transition tool lets you select an image and then apply one of 

several special effects to create one that's animated. The release I tested supported 

four types of wipes, several splits, tiling, and an interlaced effect.

GifBuilder for Macintosh 

Macintosh users will find an equally powerful tool in Yves Piguet's freeware 

application Errorl Bookmark not defined.. This program even surpasses some of the 

capabilities found in Construction Set by supporting a built-in scripting language lhat 

offers you total control over the creation and sequencing of images.lf you want lo see 

some examples of work done by other people and technical information on the GIF89a 

format, visit Errorl Bookmark not defined. and then follow the link to the GIF 

Animation Gallery. 

Java Gyrations 

Since Java is a programming language, you can have enormous control over 

the way animation sequences are performed--provided you do the programming. 

Applets, which are Java programs meant to be run from inside a Java-enabled browser 

(such as Netscape or Internet Explorer), allow you to do virtually anything with images. 

Java also includes built-in classes for manipulating GIF and JPEG images. But writing 

code to do really cool things is difficult--in any language. So why not use some pre-built, 

off-the-shelf Java classes for animation 

Which Way do we go 

The question of whether to use GIF images or Java applets for your animatton 

depends on what you want to do. If you want to use both GiF and JPEG images, tie in 

sound, support navigational control, and can rely on your users to have a Java-enabled 

browser (which will be practically everyone very soon), then Java is a great way to go. 

Applets like Animator and CltckBoard offer ready-to-use solutions lhat don't require any 

programming. All you do is create the artwork, store some Java class files on your Web 

server, and add an tag in your HTML file. 

The downside lo using Java applets, compared to GIF89a images, is the 

additional download time. The two Java applets we've described are each 

approximately 20 KB in size. Plus, they both use separate image files for each frame, If 

you had an animation sequence that required 10 images, that would mean 10 separate 

GETS your Java applet would be performing back to a Web server. Animated GIF 

images, on the other hand, are completely self-contained, with no extra code to 

download. 

What makes Enhanced CU-SeeMe great for Webmasters is that you can add a 

few lines of HTML to your page and point people to reflector software residing on your 

server, so that lhey only have to click on a link to start up their own CU-SeeMe software 

and join your conference automatically. The White Pine Reflector software, needed to 

run conferences with more than two people, is currently available on 11 Unix platforms. 

as well as lor Windows 95 and Windows NT.

For simple animations intended for Netscape 2.0 or later and Internet Explorer 

3.0, consider going the GIF route. Both GIF Construction Set and Gifeuilder are 

capable tools. For enimation purposes, ActiveX components are. for now, a relative 

unknown. They have the potential to do almost anything a Java applet can do, but 

faster. Some of the early ActiveX animation controls, such as Future Wave's 

Futuresplash, are very impressive. Expect your choices in this arena to mushroom. The 

hardest part is preparing artwork that strikes a balance between appearance and 

compactness. On the Web, the name oi the game, besides looking good, is loading 

fast. 

Produce Streaming Audio that Satisfies 

After a somewhat slow start, Web sites that are capable of delivering relatively 

tow-bandwidth audio content are appearing with greater frequency, most llkely in 

response to the increasing number of multimedia-capable PCs hooking into the 

Internet. The current offerings from some of the major suppliers of Internet audio 

software now include the ability to stream live audio across the Net, typically through 

14.4 Kbps and 28.8 Kbps modems, which in turn has fueled the growth of Web "radio" 

programming and other real-t~me content. 

There are a number of different approaches taken for Internet-based audio 

delivery. Sewer-based audio solutions are currently the only way to stream live audio 

on the Internet. Most people will find the installation of a sewer to be the least 

complicated component of delivering audio. The server install is somewhat similar to 

setting up a httpd server, using a stand-alone daemon and a configuration file that is 

read on initialization, which specifies the root location of the encoded audio files. In this 

column, we are going to focus on the process of encoding audio and delivering it from 

your Web site, using the Rea!Audio 2.0 server and audio tools as an example, which I 

recently tested for use on the W Q Web Connection. 

Preprocess Before Encoding 

When uslng pre-exlsllng source ~t IS not uncommon to flnd d~gltal aud~o f~les 

that are hundreds of megabytes or more In slte Be sure that you have sufficient hard 

dlsk capacity for both the source and final encoded aud~o content Gwen the relatively 

low cost of hard dr~ves, ~t IS wlse to conslder a mlnfrnum of a gtgabyle capac~ly to 

process your content w~th, lf you are entertalnlng thoughts of hour-long aud~o files If 

you are plannlng to archwe your source mater~al a tape backup IS essential 

Encoding Audio 

Once you have finished preprocessing, the encoding process itself is eesy. 

When using the RealAudio encoder, select the target bandwidth encodlng that the

source should be processed with. RealAudio servers have the ability to negotiate 

content delivery based on the RealAudio Player's setting, and deliver either a 14.4 Kbps 

or 28.8 Kbps bandwidth selection. Accordingly, this also means that you have to 

encode each source twice if you plan to offer users the choice of negotiated content 

delivery. There are still quite a few users that surf the Web using 14.4 modems, but the 

audio quality of 28.8 is noticeably better and should be offered if at all possible. 

Producing usable audio can be a trying experience, particularly when you 

realize that the audio quality at best will be on par with a mono FM signal. That being 

said, properly-prepared audio can add a high degree of quality to the experience 

someone has visiting your site. It takes time and patience to produce good audio 

content. 

Puttlng Vldeo on Your Web Slte: 

The Baslcr 

Video is a medium that is as direct as print and catches more attention. If your 

company has something to say with video, that video should be on your Web site. This 

year, exciting new plug-ins and helper apps for Netscape Navigator make it possible to 

inlegrale video into your Web page, making it more like a CD-ROM. Other helper apps 

make it possible to "stream" video. Streaming video is attractive to many, because even 

though It Is much lower quality, there is hardly any wait for download. 

Although il's time-consuming, the process of digitizing, editing, and uploading 

your video files is not an extremely complicated process. The only thing that should 

scare you about the process is the bandwidth that you will be using (and the legal 

problems of posting clips that may not belong to you). Before you get serious about 

doing thls, you should ask yourself: What is the value the video adds to the Web site 

Does it justify the effort spent digitizing the video and making it ready for the Web W~ll 

people who come lo the Web site actually spend their time downloading it At 28.8 

Kbps, a 1 MB file representing a few seconds of video will take about 10 minutes to 

download. Spend a day or two surfing the Web looking for video files, and download as 

many as possible to get a good picture of how and why other people are using video on 

the Web. 

There are three main video file types that you will encounler on the Web: 

QuickTime, AVI, and MPEG. MPEG and QuickTime are most commonly found, with 

QuickTime probably being the most popular; many large entertainment sites (such as 

Errorl Bookmark not defined., Errorl Bookmark not defined., and Errorl Bookmark 

not defined.) use QuickTime exclusively.

AVI is a Windows-oriented video format that is not used as much as QuickTime 

or MPEG because of problems with syncing up audio and video. For this reason, AVI is 

the least popular of the three main file formats on the Web. Easy conversion from the 

other formats to AVI is available. Since QuickTime is readlly available for Wlndows as 

well as the Macintosh, the need for AVI is rapidly vanishing from the Web. 

MPEG's (Ermrl Bookmark not defined.), main advantage over QuickTime is 

the extremely high output quality. MPEG was developed as an international standard 

for use in CD-ROMs, video games, and other media that require quality digital video. 

For the tradeoff of using slightly larger files, you get much higher-qualtty video, with up 

to 30 frames per second (the same as standard American N). 

Process Your Video 

The first step in the process is finding video to process. The higher the source 

quality, the higher the results after you digitize it. So try to get source thal is htgher 

quality than VHS, possibly Hi8 or even Betacam. Hi8 is probably su~table for most Web 

projects. If you work in the entertainment industry, you no doubt have access to higherquality 

equipment than Hi8. 

If you want to work in QuickTime, digitizing is not a problem Many Macintosh 

systems come with built-in AN equipment that makes digitizing video as easy as 

plugging in a video source and having enough disk space. Error1 Bookmark not 

defined, makes the extremely popular Videovision board, which is a hardware solution 

for video capture. 

When capturing wdeo for use only on the Web, cons~der the size of your movie. 

Unlike CD-ROM, you probably are not shooting for full-screen vtdeo wtth the best 

resolution possible from QuickTime. Instead you are trying to get a small, light image 

that looks good with compression. Using the plug-in to embed QuickTime in your Web 

page makes a great impact, but you have to plan ahead of time as to how large or 

small you want the movie to be. Choose standard sizes to capture video; for the Web 

the standard is a small 160x120 pixels. 

Sound Advice 

Sound is a very important element in video that has been sadly neglected by 

many people. Your best bet for achieving quality sound is to get an audio-editing 

software package, and treat the sound in your video as a separate element that needs 

special attention. Separate the audio from your video (in QuickTime the easiest way to 

do this is with MoviePiayer 2.1 and exporting the audio to AIFF). Listening to the audio 

separately with headphones (preferred) or decent speakers gives you a better Idea of 

what people will hear. W'tether or not people who download the video actually pay

special attention lo the audio separately is not the issue; poor audio quality will affect 

their overall impression of the video quality. 

Tools like SoundEdit 16 from Errorl Bookmark not defined. allow you to 

remove the sound from QuickTime files and edit it like regular audio, adding filters and 

equalization that will be necessary to get powerful sound out of your video. Another 

important feature in the latest release of SoundEdit 16 is built-in IMA sound 

compression for QuickTime, which allows 4:l compression of the audio track in movie 

files. 

The final process of getting your video digitized and ready for the Web is 

compression. For QuickTime there are several applications that just handle 

compression. The most popular compression is Errorl Bookmark not defined., a 

cross-platform compressionldecompression software package that has been used by 

many companies (including Errorl Bookmark not defined., makers of PC audio and 

video equipment). Cinepak is the best compression method for most video needs, 

although using it can be time-consuming, and balancing image quality and compression 

can be tricky. On the audio side. the previously mentioned IMA supports 4:l audio 

compression at 16 bits of resolution. This allows your audto to sound great while not 

becoming a burden in terms of bandwidth. 

Upload Itl 

Once you have produced your video, getting it on the Web is an easy process. If 

you use an Internet Service Provider, find out how much dlsk space you are allowed to 

use. if you have several large video flles to upload, you may be exceeding your disk 

quota. Most lSPs have a quota on bandwidth as well, and if your videos are popular, 

you may break this quota. A typical quota is transferring 200 to 300 MB a day. If you 

have a 2 MB movie file, it will take only 100 downloads a day to exceed your quota. 

After uploading the file, you'll have to create a link to it on your Web page. 

Pages with video commonly will have a JPEG screen shot of the video at the actual 

size (sometimes people will enlarge the image, but this fools people into thinking the 

video size is larger than it is). Next to the screen shot, tell the viewer what format Ihe 

video Is in, ~(s length in minutes, and how much disk space it takes up. Leaving out this 

information will hurl your chances of people actually viewing the clips, as people don't 

want to download sotnelillng they are not sure about. As a final check, download the 

file yourself, using several different viewing programs, to make sure it works with all of 

them from the Web.

Streaming AudloNideo 

"Streaming" audio and video over the Web has received lots of attention this 

past year. It started with Errorl Bookmark not defined., which allowed streaming 

audio. The quality was AM or worse, but it allowed near-instant playback without waiting 

for a full download, and this caught a lot of people's ears Shortly after RealAudio 

became popular. Xing Technology released Errorl Bookmark not defined.. which 

claims to deliver streaming video over even 14.4-Kbps modems. Over a faster 

connection, like a TI line, I was able to get a large color image that was very out of 

sync with the audio, with audio qual~ty that was about the same qualily of RealAudio. 

This level of video quality would not be acceptable with conlent like sporting events and 

actlon films, but for a live event such as a press conference it is very suitable. 

The concept beyond these stream~ng technologies is that complicated 

compression software is Installed on the server side that encodes the video so that it is 

able to be sent to the client for real-time presentations in spite of severe bandwidth 

I~mitations. The client IS expected to download helper apps that can read the 

compression type that the server software is sending The helper apps are usually 

given away free to encourage a large user base. The server software is given out for 

trial per~ods and is usually pretty expensive for full ve~.sions

WORLD WlDE WEB, THE INFORMATION STORE HOUSE 

Bijaya Kumar Panda', Ashwinl Kumar Nayak*, 

A. K. Roy" and P. K. Satapathy*' 

MCA Third Year Students of IGNOU (Utkal Univenily Sludy Centre) 

"Computer Section 

Central institute of Freshwater Aquaculture 

Kausslyagsnga, Bhubaneswar 757002 

INTRODUCTION 

Traditionally, lnternet had four application as follows: 

E-mall:The ability to compose, send, and receive electronic mail has been around 

since early days of ARPANET and is enormously popular. 

News: News groups are specialised forums in which users with same interest can 

exchange messages. Thousands of news groups exist, on technical and 

nontechnical topics. 

Remote Login: Using telnet, Rlogin or other programs, users anywhere in the lnternet 

can log into any other machine on which they have an account. 

File transfer: Using FTP programs, it is possible to copy files from one machine on the 

internet to other machine. 

Until 1990's the lnternet was largely used by academic, Government and 

industrial researchers. One new application called World Wide Web(WWW) brought 

revolution in lnternet and brought millions of new non-academic users to the net. 

WHAT IS WORLD WlDE WEB (WWW) 

The WWW is an architectural framework for accessing linked documents spread 

out over thousands of machines all over the Internet. It is a huge collection of 

interconnected hypertext documents. A hypertext document is a document that contain 

hot links to other documents. Hypertext links are usually visible as highlightedlunderline 

words in text, but they can also be graphics. 

BIRTH OF WORLD WlDE WEB 

The web began in 1989 at CERN, the European center for nuclear research. 

The initial proposal for web of linked documents came from CERN physicist Tim 

Berners-Lee in march 1989. The first prototype was operational eighteen months later. 

In December 1991 a public demonstration was given at the Hypertext '91 conference in 

San Antonio, Texas. The first graphical interface, MOSAIC, was released in February 

1993.

WHAT IS WEB PAGE 

As mentioned earlier the web consists of a vast world wide collection of 

documents. These documents are called Web pages or simply Pages. Each page may 

contain links to other related pages anywhere in the world. 

In addltion to having ordinary text and hypertext, web pages also contain icons, 

line drawings, maps and photographs Each of these can be linked to another page. 

Clicking on one of those elements causes the browser(Programs which enable us to 

view pages) lo fetch the linked page and display it. The steps lhal occur between the 

user's click and page being displayed are as follows. 

The browser determ~nes the URL(Uniform Resource Locator ) by seeing whal 

was selected. 

The browser asks the DNS for IP address of the concerned server 

DNS replies with the IP address. 

The browser makes a TCP connection to port 80 of the concerned sewer. 

It then sends a GET file command. 

The concerned server sends the required Itla. 

The TCP connection is released. 

The browser displays all the text In the {lie. 

The browser fetches and displays all images in the f~le 

WHAT IS HOME PAGE 

For a user the home page IS the starting pant for exploring a single site on the 

whole WWW. It can be thought of as a kind of "Main Menu". A homepage outline your 

options- at least moving along the hnks from this site to other po~nts of i~:!erest, as 

imagined by the publisher of this site. To whomever publishes 11, the homepage is a 

part of advertisement, part of directory and a part of part of "reference librarian". 

Just to clarify lhings a bit, a website may be a s~ngle page or a collect~on of 

pages. The main page among a number of pages is the homepage A web server is 

the machine and software lhat house lhe web site. In feebly a home page is e 

hypedext document Ihet has links to ofher points on Ihe web. 

The web is based on two standard. The HlTP protocol and HTML language. 

HTTP stands for Hypertext Transfer Protocol and it describes the way that hypertext 

documents are fetched over Internet. The HTTP protocol consists of two fairly distinct 

items: the set of requests from the browser to servers and a set of response going 

back the other way. All newer versions of HTTP supports two kinds of requests: simple

equest and full request. A simple request is just a single GET line naming the desired 

page, without the protocol verslone. The response is the raw page without any headers, 

no MIME and no encoding. The H'ITP was designed with an eye to future object 

oriented applications. HTML is the abbreviation for Hyper Text Markup Language and 

it specifies the layout and linking command present in the hypertext documents 

themselves. 

HOW TO WRITE A WEB PAGE IN HTML 

In HTML a user can produce web pages that include text, graphics and pointers 

to the other web pages. Web pages require mechanisms for naming and locating 

pages. Each page is assigned a URL that effect~vely serves as the world name. 

Ex: 

http'-:&Qlabouvhlslorv.html 

1 1 1 1 

protocol sewer address port no 

directory and file name 

A proper web page consists of a head and body enclosed by 

HTML> ....... tags. The commands inside the tags are called directives. HTML 

tags have following format. 

to mark the beginning and marks the end of it. 

Some popular tags are given below: 

TAGS - 

Declares the web page to be written in HTML. 

Delimits the pages head 

Defines the title 

Delimits the page's body 

Deltm~ts a level I header. 1=1..6. 

Set ... in bold face 

Sel..in italics 

Bracket an unordered list 

Bracket a numbered list 

Bracket a menu of 

Start a list of item 

Force a break

Form 

- 

Horizontal 

......*RE> 

Do 

 

Load 

-=A HREF=' ....' >..,

Include thumb nails for large downloaded images 

Remember that people will access your page using different browsers and different 

platforms 

Keep file names short: make them consistent 

Tell people the size of downloadable 61es if you include them 

Findout if you need permission to use text or images created by someone else 

Establish who is going to webmaster and make link on your page leading 

webmaster 

Build prototype and test thoroughly 

Announce and publicize your page where possible

Designing and planning Your Database 

In designing a database you plan what tables you require and what data they wiit contain. 

You also delemine how the tables are related. 

You must determine what things you want to store information about (eech one is an entily) 

and how these things are related (by a relationship) A useful technique In designing your 

database is to draw a pidura of your tables. This graphical display of a database is called 

an Entlty-Relationship (€4) diagram. Usually, each box in an E-R diagram ccrmsponds to a 

table in a relational database, and each line from the diagram mrresponds to a forelgn key. 

Entity 

Each table in the database describes an entity; it Is the database equlvatenl ol a noun. 

Employees, order Items, departments and produds are all examples of entities represented 

by a table in a database The entilies that you build into your database arise from the 

adivities for which you will be uslng the database, whether that be lracklng $ales calls. 

malntainlng employee infomation, or some other adhky. 

Relationship 

A relationship between entities is the database equivalent of a verb. An employee Is 

associated with a department, or an offtce is located In a city Relationships in a database 

may appear as foreign key relationships between tables, or may appear as separate tables 

themselves. The relationsh~ps in the database are an encoding of rules or praclicas 

gweming the data in the table. If each department has one department head. then a sinple 

column can be buin into the depslrhent table to hold the name of the department head. 

When these rules am built Into the drudure of the database, there Is no pmlsion lor 

exceptions: there is nowhere to put a semnd department head, and duplicating the 

department entry would involve duplicating the deparlmenl ID. wh~ch is the prlmary key. 

Relationships between tables 

There are three kinds of relalionship between tables: 

One-Imny relationship 

Onelo-one relationships 

. Many-to-many relawnshlps 

Them am five major d ep in We dwn process. 

Step 1: identify entiUes and relationships 

Step 2: identify the required dsts 

dep 3. nomlize the data 

Step 4: resolve the Wonships 

SIep 5: verify the d&jn

ldenttfy entities md relationships 

To idun\Hy the entities in your design and their relatbnshlp to each other: 

1 .Define high-lewl actlviU.s. ldenbfy !he general erne you will ma thk3 dalebase for. 

For exarnfle, you may want to keep trad of infomation about employees. 

2.ldentify entities. For lhe Hsl of aduities, Identify the wbjed areas you need to maintain 

information abouL These will become taMes. For example. hire employees, essign to a 

department, and determine a sWU level. 

3.ldentify relattonrhips. Look at the adiiities and determine what the rela(ionships will be 

between the tables. For example, there is a relationship between departments and 

employees. We glve this relationship a name. 

4.Bre.k down the activities. You started out with htghlwel adivies. Now examine these 

acllviiies more arcfully lo see If some of them can be broken down Into lower-level 

act~iiles. For example, a Iilgh-level activity sub as maintaln employee information can be 

broken down inlo: 

1 .Add now employees 

2.Chanpe existing employee information 

3,Delele terminated employees 

To identify the required data: 

1 .Identify supporting dala. 

2.Llst all tlie dala you will need to keep track of. The data that describes the table (subject) 

answer8 the questions who, what, where, when, and why. 

3.Set up data for each table. 

4.Llst the evailable data for each table as il seems appropriate righl now. 

5.Sei up dais for each relationshlp. 

0.List the data that applies lo each relationship (if any). 

Nonnallze th* data 

Normallzatior~ Is a series of tests you use to eliminate redundancy In the data and make 

sure the data is associated wtth the coned table or relatlonshlp. 

To normalize the dala: 

1 .List tha data: 

2.ldenllfy at least one key lor each table. Each table must have a primary key. 

3.ldmtlfy keys for relatlonshlps. The keys for a relaUonshlp am the keys lrwn the two tables 

it joins. 

4.Check for calculated dala in your supporting dala IW. Calculsted data is noi normally 

stored in the datab.se. 

S.Pul data In nnl nonna) Ion: 

6.Remwe repeatlng dala fmm tables and relationships. 

.Create one or more tables end relalionships with the data you remwe. 

0.Put data In second normal lorm: 

9.idenlWy tables and relationships with mom than one key. 

10.Remwe data that depends on only one par! of the key. 

11 .Create one or more tables and relaUonshlps wiM the data you rumwe. 

12.Put data In third normal form: 

13,Remove dala that depends on other deta In the table or relationshlp end not on the key. 

14.Create one or more tables and relaUoruhips with the data you rumwe. 

~ut~ng dam in first n o m ronn ~ 

Remove repeatlng groups. 

To test for lint normal form, remwe repeating groups snd putthem into a table of their own.

Putting data in second ml fwm 

Remove data that does not depend on the W le key. 

Look only at tables end relationships Vlsl have mom than one key. To tesi for second 

normal fonn, remwe any dala that does not depend on the whale key (all the cdumns thal 

make up the key). 

Putting data in thkd noml 

form 

Remove dab that doesn't depend diredly on the key. 

To test for thild normal form, remove any dala that depends on other date rather than 

diredly on the key 

resolve the relationships 

When you finish the normalization process, your design is almost cwnplele. AH you need lo 

do is resolve the relationships. 

5 

Resolving relationships that carry data 

Some of yo esolving relationships thal carry date 

Some of your relationships may csny dala. This snuation oRen ocwrs in many-to-many 

relationships. , 

-- I. 

-. I" 

When this is the case, change the relaUonship to a lable. Thq key to tho new table mains 

the same as It was for the miationship. 

Rarolvfng rol#Uonrhlprr Ih8t do not cmy data 

In order to Implement relationships thal do not cony data, you need to daRns forelgn keys. A 

fonlgn key Is a column or set of columns thal wnlalnr prlnury key values from another 

table. The fmlgn key allows you to aces, data frwn more than one table al one Ume. 

There are some baelc rules that help you dedde where to put the keys: 

One to many In a one-to-many relalionship, the primary key In the one Is canled In the 

many. In this example, the fomign key goes into the Employee table.

One to one in a one-to-one relationship. the Iombn key can go into enher table. If I is 

mandatory on one Me, but not on the other. I( shouM go on the mandatory side. In this 

example. the forelgn key (Head ID) is in the Department table bemuse # is mandatory 

there. 

-..I- 

Many to many In a many-temany relalionship, a new table is created with two foreign keys. 

The existing tables are now related to each other through lhls new table. 

Choosing primary and foreign keys 

The primary key is the column or columns that uniquely identify the rows in the table. If your 

tables are properly normalized, a primary key should be defined as part of the database 

deslgn. 

A forelgn key is a column or sel of columns that contains primary key values from another 

table. Foreign key relationships build one-to-one and one-to-many relationships into your 

database. it your des~gn is properly normalized. foreign keys should be deftfled as part of 

your database design. 

verify the design 

Belore you implement your design, you need to make sure it suppons your needs. Examine 

the activities you Mentifled at the stail of the design procsscr end make sure you can access 

all the data the adhrities quire: 

Can you find e path to get all the inlomalion you need 

Does the design meet your needs 

Is ell the mquired data wadable 

If you can ansner yes to el the questions above, you am ready to implement your design

DATABASE ON FISH DISEASES 

6. B. Sahu ,A. K. Roy, P. K. Satapathy, S. C. Mukhrrjee and S. Ayyappan 

Centre1 Instilute of Freshwater Apueculture 

Keusalyeganga. Bhubaneswar - 751002 

INTRODUCTION 

Fish health related information is of vital importance in modern aquaculture. A 

system for rewrd keeping and health monitoring Is essential for successful aquaculture 

production. The basic methodology to develop animal health and disease information 

system for farm animals has been described by Hall (1978). This present system is 

designed to record diagnosis and diseases in a simple way by transferring data into 

separate files. Limitations of detail information on fish diseases, definitions 

(nomenclature) etc. have been considered and due care have been taken during 

development of the database information system. Database system to record 

exclusively fish disease events have not been reported. 

OBJECTIVES 

The system can fulfil the following objectives 

1. Effective surveillance and monitoring of health and disease status in fish 

maintained in a farm1 aquaculture pockets. 

2. Precise recording and processing of regularly gathered morbidity and morality 

data to produce comparable indtces of diseases. 

3. Rapid retrieval of disease information and identification of variations in disease 

events of individuals and in fish stock. 

4. Standardized storage of epidemiological data for retrospective studies. 

5. Assessment of impact and economic measures adopted to prevent, control, 

eradicate and treat diseases and improve aquaculture productivity. 

6. Forecasting of fish diseases and tips for aquaculture farm operations. 

MINIMUM SYSTEM REQUIREMENT 

The fish disease data and information system for organized aquacutture sectors 

needs the following minimum computer equipment (Hardware) and programmes. 

1. IBM PC with a minimum of 640 KB memory and 2 x 5.25 360 KB DSDD Floppy 

drive. 

2. Matrix I Line printer.

The dalabase formal, post-mortem report forms, dala didionary for data entry 

have been developed by Fish pathology Division, CIFA, Kausalyaganga, Bhubaneswar. 

The system includes the following scientific aspects (software): 

a) Standardize definilton of disease events and diagnosis. 

b) Systematic classification of diseases. 

c) Forms for recording data on clinical, post-mortem, fish stock (pond) 

environment and Laboratory examination. 

d) Use of standard disease indices. 

e) Formats for reporting informations regularly 

I) Computer programs (software) for processing disease data 

The disease data will be processed in MS-Excel, from which statistical data 

analys~s can be done and finally the output can obtained in graphical form. The 

RDBMS packages like ORACLUFOXPRO can be used for data entry and for 

sequential querry processing to retrieve information, E-mail can be used extensively to 

collect disease informallon at a cheaper and faster way wherever the facility is 

available. Mailing list of farmers can be maintained to provide Information of disease 

incidence and precautionary measures to be taken. 

CONTENT OF THE SYSTEM 

1. Standardize definitions of disease events and diagnosis. 

2. Systematic classification of disease. 

3. Forms for recording data at clinical, post-mortem, Laboratory examinations. 

4. Use of standard disease indices. 

5. Formats for reporting information regularly. 

6. Software for processing disease data. 

USES OF FISH HEALTH AND POND ENVIRONMENT DATA 

A source of information for monitoring health status of cultured fish stock. 

A reminder for prophylactic measures to be undertaken in a aquaculture farm 

To monitor optimal productivity of the fish farms. 

A source of information about previous Illness and therapy. 

A source of information for epidemiological research. 

A source of clinical and laboratory information. 

A source of information for planning fish health. 

A source of information for calculating cost of disease and disease control.

INFORMATION GENERATION 

Information are generated through the following records 

1. Fish stock data register 

a) Aquaculture farm/sector report 

b) Monthly weight gain report 

c) Fish stock strength report 

d) Monthly Morbiditylmortality report 

2. Listing of all d~seas events 

3. Comparative pattern of disease encountered clinically or at post-mortem. 

4. Specific morbidity mortality rates of different species, class, sex, season. 

environment, locality etc., or combinations as desired. 

Fish disease information gathering suffer from deficiencies at ail levels in India. 

The information available at presenl 1s not effective for surveillance and monitoring of 

fish diseases. An aquaculture information system for the Indian situation has to be 

developed at three organizational tlers i.e. 1. National 2. State or Regional and 3. 

Farm level. 

The uniform data generation, recording and retrieval helps in monitoring of fish 

health. However, the organizational necessities to provide routine health care, 

laboratory diagnosis, drug inventory, schedules of vaccination, deworming, d~pping etc. 

can not be over ruled. The fish disease information system at the national and regional 

levels will be similar, except possibly for the quantum of data processed. 

SYSTEM IMPLEMENTATION 

1. Fish disease information management : 

a) Organized farm level : 

The information system at organized farm levels has to be different as It will 

record and process primary data. The data base maintained at the farm level will be 

used for purpose of monitoring disease status and production efficiencies (Maw el a/. 

1990) . Recording of disease event at the farm level will be for the cultured fish in farm 

ponds. This system has been designed to record disease related data at organized 

farms engaged in aquaculture research. These farms may also be the sentinel farms 

for a national disease information system.

) Fanner parlicgalory rapid appmisal (PRA) : 

PRA approach and methods have been tried to help the aquaculture farmers to 

do their own analysis on fish disease epidemiology, surveillance and monitoring and 

make their own needs and priorities known to scientists. It has been found out that 

PRA satisfies the acute decision making needs of fish disease epidemiology, 

aurveiilance and monitoring. Participatory methods of 'visualisation', such as 

mapping, modeling, matrices, linkages and casual diagramming are powerful, valid 

and reliable when well facilitated and performed. PRA is a low cost diagnostic method. 

which can be very well applied to fish health surveillance and monitoring. PRA tool has 

already been evaluated under 'Institution Village Linkage Programe (IVLP). ClFA 

Centre, Kausalyaganga and reported (Sahu el al., 1998) (Please see Annexure ). 

CONCLUSION 

It has been felt that disease has been and will continue to be a major constraint 

to the development of aquaculture. Further it has been witnessed high loss of revenue 

due to d~sease and health related problems. So the importance of 

epidemiologylepizootiology in providing solulioi to aquaculture health problems can not 

be overlooked. Fish health diaanost~cians, - researchers and extension scientists should 

be familiar w~th on-fan-conditions, diagnostics and therapy. So that the informed 

decisions on control and treatment can be made. Further research on epidemiology 

and epkootiology of aquatic animal diseases will help to develop a comprehensive list 

and database on notifiable fieh diseases. 

1 The database is expected to provide a feed back to researchers, diagnosticians 

for making improvement8 in technology and disease surveillance. 

2. Thrust areas of need at regionallnational level. 

3, Identification of appropriate research need and refinement of methods to 

conduct flsh health research programme. 

4. Ranking of diseases and syndromes causing key production constraints in 

aquaculture. 

5. Medium range fish disease forecast can be made from time series data on 

organized farms and fish production pockets and fish farmers can be alerted 

before farm operations . 

REFERENCES 

Inglis. V . Roberts, R.J., and Bromage, N.R. (1993) Bacterial Diseases of Fish, Oxford Blackwell 

Scienlifi Publrcalion. London. 

Maru, A. Srivastava, R.S.; P. S. Lonkar, S.C. Dubey and A.L.Choudhury (1990). Sheep 

research Database, CSWRl Pubkalion, CSWRI, (ICARJ Avikanagar 304501, Rejasthan. 

India. 

Sahu. 0. B., Radheyshyam., Uuldeep Kurnar.. Mukherjw; S. C. and S. Ayyappan (1998). 

Farmer participatory flsh disease su~elllrnce and monitoring using PRA tooh, Trop&al 

AgdcuHural Resoetch end Extension, l(2) : 1 - 14 pp.

Visualisatio~i of Fish disease related infor~l~atior~ tllrougl~ PIM diagnosis 

SEASONALITY OF FISH DISEASE 

I.".," 

I.*."*. 

Il..7.r(.*. 

I..."",*.*. 

I...*.,",. 

.,I..*. 

I". 

&5-+-/J 

I I I . . . 

* . . 

UOIIVUI W PWCIDENCE 1 J.n 0.d 

4 . 1 , 1 , 

FISH DISEASE CALENDAR 

1tMI 

E U S INCIDENCE It4 VtLLAOES AflOUNO ClFA fMM

SPAWN MORTALITY 

FRY MORTALITY 

!US 

rn 

FlNOERLlNO MORTALITY 

Lulrovhlc~lion 

18% 

JUVENILE MORTALITY 

FACTORS RESPONSIBLE FOR POND FISH LOSSES

QUANTITATIVE AND QUALITATIVE FISH PRODUCTION DATABASE 

9. B Sahu, J. K. Jena, A.K. Roy and S. Ayyappan 

Central lnsl~lule of Freshwater Aquaculture. 

Kausalyaganga, Bhubaneswar-751002, Orisse 

INTRODUCTION 

Fish growth and production related information is of vital importance in modem 

aquaculture. A system of record keeping is essential for the success of the production 

programmes. The Central Institute of Freshwate Aquaculture is worklng to develop a 

Computer based system to record and proces quantitative and qualilative fish growth 

and production related events in different production systems. 

IMPORTANCE OF AQUACULTUE PRODUCTION DATABASE 

As aquaculture IS multid~mens~onal ordinary quantltatlve analysis 16 too 

Inadequate for arrivlng at any valld consclusion Phys~cal and chemlcal characterist~cs 

of the water body seed quality, denslty, season, culture system, feeding and 

harvesting pattern are the Important factors and proper management of all these 

factors are essentral for successful operation of pcsc~culture act~v~t~es Generally few 

major factors are consldered at a tlme, whlle keeping other minor factors at a known 

level Even then su~table varlance function are presently not available to compare 

product~on parameters from dtfferent water bodles to observe and compare the 

treatment effects (Royce, 1996) 

USE OF PRODUCTION RELATED DATABASE 

Among the many factor, and their interaction influencing the growlh of fish are : 

genetic make up, species, behaviour, population dynamics, endocrinology and feed etc. 

Any single factor should not be consldered in isolation even though overall opt~mising 

the various factrors is difficult. Definitive information on optimal growth is lacking for 

many culturable species. Growth rates, and qualitative and quanlitative production 

parameters under different culture condition can be recorded in a database and 

optimum condition for growth can be modelled which would serve as a guide to 

researchers and producers (Wathne. 1995). 

CONTENTS OF DATABASE 

Knowledge of production efficiencies and determination of growth potentials 

which coincide with desired carcass attributes have provided impetus for improvement 

in genetic selection and management of aquatic animals. The role of quantitative end 

qualitative carcass data in aquaculture research programmer e~pscialty, genetics and

eeding, production management, feeding and nutrition for evolving suitable 

breedistrain for quantity and quality fish production can not be over emphasized. For 

this to be accomplished, accurate, standard and uniform methods for carcass 

evaluation are critically important. The present database is prepared keeping in mind 

the information related to : (a) Physical and chemical characteristics of water bodies 

(b) seed qual~ty (c) feeding (g) quantitaive production data (growth) and qualitative 

(carcass evaluation) production technology informations. Due care has been given for 

meterological parameters also. 

DATA FILES 

The date can be mantained in following data files. 

I. Pond environmental records sub database 

2. FeedlFertilizer sub database 

3. Monthlylfish body weight sub database 

4. Meterological record sub database 

5. Fishlcarcass quality sub database 

6. FishlFlesh quality sub database 

1. Pond envlronmental record Sub data bare 

1. Sector Code : 

2. Pond accession No : 

3. Pond size (ha) : 

4. Water deplh (m) 

5. Stocking density (noslha) : 

6. Soil texture (sandylclayielloamy) : 

7. Soil available Nitrogen (mg1100g) 

8. Soil available Phosphorus (mg1100g) 

9. Soil organic Carbon (%) 

10. Dale of entry : 

11. Water transparency (cm) 

12. Water temperature ('C) : 

13. pH: 

14. Dissolved oxygen (mgfl) : 

15. Free Cerbon dioxide (md) 

16. Total Alkalinity (mg CaCO JI) : 

17. Total Hardness (mg CaCOJn ) : 

18. Ammonia nitrogen (NH, -N) (md) : 

19. Nitrite nitrogen (NO2 - N (mg/L) : 

20. Nitrate nitrogen (NO, - N) (mgfl) : 

21. Phosphate phosphorous (P205P) (mg~l) :

22. Plankton Count (NoA) : 

23. Any others : 

2. Feed I Fertilizer management Sub database 

1. Sector Code 

2. Pond accession No: 

3. Pond size (ha): 

4. Water depth (m) : 

5. Date of entry : 

6. Stocking density (noslha). 

7. Lime (kglha) : 

8. Urea (kgha) : 

9. Single Super phosphate (kglha) : 

10. Micronutient (kglha) : 

11. Manure( Cowdung/others) (kgha) : 

12. Feed (kgldaylarea) : 

13. Any others : 

3. Monthly1 Periodic fish body weight Sub data base 

Sector Code : 

Pond accession No. 

Pond size 

Water depth 

Stocking density 

Date of Weighing : 

Age (days) : 

1. Species Code ............................ wt (Gms) 

2. Species Code ........................... wt (gms) 

3. Species Code ............................ wt (gms) 

4. Species Code ............................ wt (Qms) 

5. Species Code ............................ wt (8ms) 

6. Species Code ............................ wt (gms) 

7. Others ........................................ wt(gms) 

4. Meterological record Sub database 

1. Air temperature ("C): 

2. Relative humidity (%) : 

3. Rain fall (rnmlday) : 

4. Sunshine hours (hrslday) : 

5. Wind velocity (spm) 

6. Any other :

5. Flshl Carcass quallty Sub data base 

Annexure - i 

6. FlshlFlesh quality Sub data base with indices 

Annexure -11 

REFERENCES 

Dunham, R.A (1995). International Conference on sustainable contribution of fisheries lo food 

secuirlty, Kyolo, Japan, 4 - 9 Dac. 1995, 15 - 16 pp. 

Royce, W. F. (1996), Introduction to the practices of fishery sdance, Acedemic Press. 1NC. 

Wathne, E. (1995). Stralegies for direct~ng slaughter quality of farmed Atlantic salmon (Salmo 

solar) with emphasis on diet composition and fat deposition, Dr Thesis, Agricultural 

Univenily of Noway, N-1432. Aes, Noway.

DATABASE OF INDUCTED BREEDING EXPERIMENTS ON 

AN INDIAN MAJOR CARP Labeo mhita (Ham.) 

S. D. Gupta, A. K. Roy, S. C. Rath and P. K. Satapathy 

Central lnstrtute oiFreshwater Aquaculfun, 

Kausalyaganga, Bhubaneswar - 751 002 

INTRODUCTION 

Over the past years huge data have been accumulated on the breeding 

experiment of Labeo rohita (Ham.) conducted at CIFA. An attempt IS being made to 

form a database of breeding experiments using standard techniques applicable for 

computerized relational database management system followed by multivariate 

analysis which is likely to address a variety of research questions which have not yet 

been attempted in our country so far. Summary of parameters studied and preliminary 

results are presented below. 

Labeo rohrta (Ham ) IS the most consumer preferred culturable lndlan major 

carp belongs to famlly Cypnnldae As llke other lndlan major carp Labeo rohrfa (Ham ) 

do not breed spontaneously In the confined water of culture pond, but breeds In nature 

In flooded river durlng monsoon Its non-spontaneous breedtng In captive water may 

be due to Inadequate secretion of gonadotropln, a hormone of ~ts own pltultary Thus 

an exogenous lnductron of hormone for breedlng In capllve water known as Induced 

breedlng The prlnclple of Induced breed~ng IS to manipulate the gonadotropln proflle of 

the ~ndtv~dual to the deslred level by adrnln~strat~on of pttultary extract of other specles 

or Isolated concerned hormones 

lnduced breeding of Labeo rohrta(Ham.) ever since 1957, the initlal success of 

induced breeding of Indian major carps by Choudhuri and Altkunht, has began a new 

era in Indian carp culture. Induced breedlng by administrat~on of pitullary extract's 

popularly known as induced breeding by hypophysation. To standardize the 

technology of induced breeding and to produce adequate quantity of seed of Labeo 

rohita (Ham.) several breeding experiments have been conducted, but no database is 

available on the subject. The present communication is an attempt to create some 

database on induced breeding of Labeo rohita (Ham.). The study pertains to 462 

experiments, from July, 1970 to August, 1982 with carp pituitary extract (CPE), 

noncarp piluitary extract (NPE) and gonadal concerned hormone (GCH) as inducing 

agents. Again the inducing agents have been adminislered in different combination 

and in different protocols. Experiments have been conducted within the temperature 

range of 27.5 to 35°C. Brood body wt. ranges from 0.3 - 2.7 kg (Male) and 0.4 - 3.5 

kg (Female). Spawning fecundity varies from 0.03 lakh eggslkg to 4.18 lakhslkg body 

wt. of the female. Fertilization rate ranges from 0 to 95 percent and spawn recovery 

ranges from 0 to 2.83 lakhdkg body wt of female.

INDUCING AGENTS AND SPAWNING RESPONSES 

Twenty seven types of inducing agents have been used in 462 breeding 

experiments. These inducing agents are broadly classified as carps pituitary extract. 

noncarp pitu~tary extract and marine fish pituitary extracts. Pituitary extracts with 

~solated hormone, in combination with salmon pituitary powder etc. Again carp pituitary 

extract in aqueous medium for immediate use and in glycerine medium for instant use. 

Glycer~ne medium extracts have been tried after 0 year. 1 year, and 2 years, 3 years, 

4 years and 5 years intervals. 

Table 1. Spawnlng response In Labeo rohita (Ham.) with different lnduclng 

hormones 

CPE 

GCH 

Spawnlng 

Percentage 

Inducing Agents (+)Tive (-)Tive Remarks On Negative(-) 

Spawnlng 

Acetone preserved 79.7 20.3 Inadequate diet and improper 

carp pituitary in queous 

gonadal maturation 

extract (ACPAE) 

Carp pituitary aqueous 88.9 11.9 High temp., unripe gonads, 

extract (CPAE) 

incorrect doses 

Carp pituitary glycerin 57.2 42.8 High temp., unripe gonads. and 

extract (CPGE) 

some other unknown factors. 

Pituitary extracts and 52.4 47.6 Loss of potency in more than 

other hormone 

two years, adverse weather 

combination (PEOMC) 

condition, and improper gonadal 

maturation 

NPE Noncarp pituitary 39.6 60.4 Pituitary extract other than 

extract (NPE) 

freshwater catfishes, carps and 

salmon and single dose of 

salmon pituitary powder. 

TEMPERATURE AND BREEDING RESPONSE 

Water temperature plays a vital role in carp breeding. In the present study of 

Labeo mhifa breeding 73.5% of breeding failure is attributed to the water temp r 32%. 

Only 26.5% of the non responded instances found in the temperature s 31.5'C. 

FERTILIZATION EFFICIENCY 

Spawn production depends upon the rate of fertilization of the ovulated eggs. 

Fertilization efficacy i 50% is considered as poor fertilization Instances (PFI).

SPAWN RECOVERY 

In the present study the fertilized eggs are incubated in both out door hapa 

system (OHS) and in Indoor hapa system (IHS). If spawn recovery r 70% out

THE MILLENNIUM BUG OR THE Y2K WAR 

A. K. Roy 

Eiohfmatics Centre 

Cenfml lnslilule of Freshweter Aquaculfure 

Kausalyaganga, Bhubaneswar 751002 

INTRODUCTION 

Y2K is an abbreviation which stands for 'year two thousand' (K is representative 

of a K~lo which is equivalent lo a thousand). The Y2K problem is also known as 

MILLENNIUM BUG. The year 2000 (Y2K) problem may be defined as the inability of 

computer program to correctly interpret the century from a date which represents an 

year as a two d~g~t value. The war - 'THE Y2K WAR' deals with simple problem that 

involves just two d~gits. A wide variety of computer programs that display, manipulate 

or store dates have adopted the shorthand convention of using only the last two digits 

of the year Many of these programs will fail when using dates beyond 1999, 

parlrcularly if they compare those dates with earlier dates. It is estimated that the effort 

required to identify and fix the problem in all systems may take several years and 

thousands of programmer's hours to complete. This paper describes types of problems, 

misconceptions, apprehensions, remedies and opportunities associated with the Y2K 

problem. 

BACKGROUND OF Y2K PROBLEM 

The majority of computer applications is use today were developed years ago 

when the year 2000 seemed to far in the future to worry about. These programs 

historically represented the year portion of a date using only two digits. Dates are 

critical to computers. Most dates programmed in computers are based on a two-digit 

year field for instantce '99" rather than '1999". There are two main reasons why a twodigit 

field has been the norm among programmes over the last 50 years, firstly, the high 

cost of storage in the early days of computing and secondly as systems and 

applications were constantly being developed and replaced, it was never realised lhat 

they would last till the advent of new millennium. Some believe lhat this problem is 

partly due to farsightedness and partly due to lack of resources. The problem exists 

for mainframe, mid-range and PC computers alike. The two-digit year field can be 

found in microcode, operating systems, software compilers, application queries, 

production screens and data bases. The problem was not thought of earlier, but it was 

realised when some sobare which deals with future dates i.e. renewal dale, License 

expiry date etc. started giving problems. 

As believed, the year 2000 problem comes from, but not limited to, the use of a 

2-digit year (yy) format, instead of a 4diiit (yyyy) format for year representation within

programs, databases, files and procomes. As for an example. the year 1997 is 

repm~nted as '97'. The year 1998 as '98, and so on. Likewise February 29, 2000 is 

represented as 02/29/00 (using MMDDW format) which might bs interpreted as 

February 29.1900. Consequently, programs those perform arithmetic operations, 

comparisons or sorting of date klds to yield correct results when manipulating dates in 

the year 2000 and beyond may be affected. 

Some of the misconceptions about the year 2000 challenge with clar~ficalion are 

as follows. 

i) That the problem occurs only when or after the century rolls over 

ii) 

iii) 

That it is a hardware clock problem whrch should be solved by computer 

vendors. 

That this is a problem that occurs only in mainframe systems and or core 

application 

i) In forecasting applications thal deal with fulure dates will face problems In 

advance of the year 2000. Cases that deal with expiration dates that go beyond 

the 2000 are already at risk. 

ii) 

iii) 

iv) 

Contrary to the bel~ef that it is a hardware problem, in realty the problem comes 

mostly from application programs. 

Any program or system can be affected if it uses only two digits for 

representation of year in any file, database, logs wilh 2-digit year fields and any 

data entry, update and output processing that employs 2-digit year fields. 

Y2K problem will have impact at all levels in Hardware level, operation system 

level and application software level. 

THE NATURE AND STRATIFICATION OF THE PROBLEM 

The year 2000 problem (phenomenon) has broad impact and can be visible in 

various ways. This phenomenon has both a information processing systemwide and an 

institutionwide impact on computing environment. Within system, this phenomenon can 

originate from or affect many key components like hardware, software, people, data 

and procedures. Instlutionally this can act as the contaminated data files to other 

computing systems inside or outside the organizations. This is a complicated problem 

wilh far reaching consequences but it is not beyond solution. This problem may also 

affect microcoded hardware like VCR and digital clocks. The year 2000 syndrome is

compounded by many varialions used to ex- year and date notatio~ in data, the 

mathematical calculations performed on thoae data notations and in many places 

where date data may occur. These variations are stratiii as follows: 

w: Likely problems may be encountered when the 1st two digits in a 

year are assumed to be 19 and ignored during data entry, manipulation or hard 

coded on output. 

P u r e d Sometimes special values of the last two digits in a 

year might be used for a special purpose, for example 99, 365199 or 12.31.99 

might be used to indicate 'no expiration date' or 00 to indicate an 'unknown 

year'. 

incorrect Many programs determine the date format (MM 

DD YY or DD MM YY or YY MM DD) by testing an appropriate part of the dale 

field. A value of zero might be considered as lack of any date at all. 

Arilhmrllc: Many arithmetic calculations that operate on dates with 2-digit 

year representation might have potential danger. A person with a birth year of 

1951 will be considered to be 51 years old rather than 49 years old in 2000 if 

the year 1951 and 2000 are represented by 51 and 00 respectively. 

SPdlng; When two digits are used to represent a year, programs that collate year data 

will sort that data out of sequence if there are dates both before and afler the 

year 2000 transition. 

Archival: Data arch~ves like magnetic tapes of data bases containing students 

records or research data or financial records may have fixed 2digit year data 

should not be modified. Instead special program may be written to read and 

convert archival data particularly if the data are to be used in union with data 

from beyond 1999. 

D s t a a x c h n n g a ; When data are to be exchanged between systems, there occurs a 

special case of the year 2000 mitigation. There must be close co-ordination 

between systems updates on both sides of exchanges otherwise the receiving 

systems may fail. 

Sometimes date information is used by the system as 

part of their algorithm to generate a unique key or serial number. If a 2 digit 

year is used, thls may cause confusion in some cases. This type of problem is 

likely to be an issue only with datasets covering more than 100 years.

Lrar,: This is not a 2digit problem rather a problem in the year 2000, 

2400 etc. The year laOO is not a teap year because it is not a multiple of 400 

but 2000 is a leap year. Date conversion routines may not have been 

programmed to take into account this anomaly since it occurs only once in 400 

years. 

Some of the problems caused by the identification of the 2000 as a non-leap 

year that would manifest in dates after February 28 are as follows. 

i) Dav - - calculations (the year 2000 has 366 days not 365) 

ii) -of- 

the - N& 

iii) 

calculations (March 1, 2000 is a Wednesday, not a Tuesday 

which is February 29,2000. 

Week calculation: 

The 1 lth week of the year 2000 is 5 through 1 I March. not 6 through 12 March. 

APPREHENSIONS AND REMEDIES OF Y2K CRISIS 

The impact will be tremendous not only for the business community but for the 

community at large. All the areas like banking, budgeting, accounting, stock market 

licensing, reservations, inventory, credit card transaclions, forward planning will be 

affected due to Y2K crises. 

The dimensions of this challenge are enormous Gwen the societies reliance on 

computers, the failure of systems to operate properly can mean anything from minor 

inconvenience to major problems. Licenses and permits not issued. Payroll medical 

and academic records malfunctioning. Errors in banking and finance. The bug affects 

computations which calculate age, sort by date, compare dates or perform other 

specialised tasks. 

Some software vendom have developed modern tools as a remedy in the 

process. But these are not guarantee to solve all problems but will likely identify where 

problems exist and recommend solutions, speeding the process . 

STRATEGlES FOR ELIMINATION OF Y2K PROBLEM 

For running application softwares in 21st Century a strategy should be decided 

for making the systems Y2K compliant. An inventory of all such softwares has to be 

made and classified keeping in view the following points. 

i) Whether the software will run beyond year 2000 

ii) 

Whether the softwares involve computations on same future dates.

iii) 

iv) 

Whether, the existing softwares can be replaced by the other versions apart 

from being Y2K compliant. 

Whether all such sohares which are yet to be developed in such a way that 

they are Y2K compliant. 

It is clear that not everything has to be converted. Communicating tools and 

hardwares also has to be Y2K compliant became these involve date and time. There 

are some softwares which are very critical. These are the real time systems like flight 

monitoring, the computers of aircraft, spacecraft and radar system etc. 

Business Opportunity 

According to the experts, solutions to the Y2K crisis may yield huge commercial 

opportunity. Conservative estimate put the global opportunities in this area at $ 60 - 

100 billion, lnd~a may caplure a business worth 2 - 5 billion. Therefore, It is a bright 

challenge of the Indian I T professional.

SCOPE OF APPLICATION OF STATISTICAL METHODOLOGIES 

IN AQUACULTURE RESEARCH 

A. K. Roy 

Biornlwmetics Centre 

Central Insbtute of FreShWter Aquecullun, 

Keusalyegenge. Bhubaneswar 751 002 

INTRODUCTION 

Like many other disciplines of science, statistics also plays an important role 

in Aquacultural Research. Some of the areas where statistical methodologies can 

be applied are described below. These are based on the experience of the author. 

There may be some more areas which are not included in this article. 

SYSTEMATIC STUDIES AND IDENTIFICATION 

In systematic studies it is always necessary to establish the relationship 

between two or more morphometrical quantitative measurements, like relationship 

between head length and total lenglh or breadth of carp, total lenglh and carapace 

length of a prawn etc. 

Taxonomic hypothesis formulated in terms of quantitative characteristics may 

be tested by means of chi-square test, student's 1-test, analys~s of variance, mult~ple 

range and non-parametric tests. Multivariate analysis may be useful when it is 

necessary to combine information on several characters (morphometridmeristic) to 

obtain best possible racial discrimination. 

COLLECTION, ESTlMATlON AND TRANSPORTATION OF FISH AND PRAWN 

Till today freshwater aquaculture in India is partially dependent on natural 

production of carp seed. Therefore, a lot of work is there for standardisation of 

collection, estimation and transportation of fish end prawn seed. Availability of seed at 

different locations may be dependent on current velocity, turbidity, dissolved oxygen. 

food availability and numerous other factors. To identify the factors responsible for 

the availability of seed and to select suitable place for collection, stratified random 

sampling technique. Chi-square test, analysis of variance and multivariate analysis 

can be applied. Factorial experiments can be planned for optimisation of space, 

time, temperature etc. for mortality free transportation of fish seed to different area 

where simultaneous effect of various factors can be studied precisely taking into 

account environmental condition and bioassay techniques can be applied to assess 

the impad of affluents on fish larvae. SuRable sampling techniques for 

estimation of fish seed may be employed.

NURSERY REARING AND CULTURE EXPERIMENTS 

Aquaculture experiments are quite different from those of agricultural 

experiments because in the former case experimental animals can not be seen and 

periodical mortality cannot be observed. Moreover, requirement of minimum 

experimental units can never been met due to the shortage of ponds. However under 

varied level of fertilisation, stocking density, species combination and ratio, 

supplementary feed during different stages of nursery and culture experiments. 

simplest designs like completely randomised block design, randomised block 

design, latin square design, factorial design, incomplete block design etc. depending 

on the objective of the study can be laid out. System approach and simulation 

sludies may also be adopted for studying overall impact of stocking size and 

density, feeding quality, quantity, periodicity, species composition in polyculture 

and pond management to increase carrying capacity of water bodies. Manipulations of 

nonmonltory inputs may enhance profitability. 

OPTIMUM UTlLlSATlON OF BROODER 

Size of brooder, dose of pituitary gland and physiochemical parameters of pond 

plays a great role during breeding. Therefore this is one of the area where through 

utilisation of suitable design of experiments, optimum exploitation of brooders can be 

done. 

ESTIMATION OF FISH POPULATION 

For rational management of culture fishery, monitoring of numerical changes 

which occur in a population through the course of time is essential for basic 

understanding of population number and production. For precise estimation of 

fish population number from pond at any point of time the following methods may be 

applied on which a lot of research work has been carried out at this Institute. 

1) Method of two successive hauling 

2) Mark-Recapture method 

i) Method of two ruccessive hauling 

This method is very simple. The whole thing is to be done is that drag a net 

once in a pond then keep the capture fish in a container and let the catch be N, 

numbers then drag the net again in the same waterbody and let the catch be N2 

numben. Then the estimate the total number of fish present in the pond is given by

This method being convenient in operation involving minimum cost 

recommended for operation with caution (Roy et el.. 1995). 

ii) Mark-Recapture Method 

The rationale underlying mark-recapture experiments to estimate population 

number is that the proportion of marked fish appearing in a random sample 

provides an estimate of the proportion of marked fish in the population. If 'm' is 

the known total number of marked fish in the population from which the sample was 

drawn, then division of 'm' by the estimate of proportion marked given an estimate 

of total number of individual in the population. Mathematical expression of the 

estimation formula becomes (known as Petersen Method). 

where N = total number of fish in the population (unknown) 

m = total number of marked fish in the population (known) 

c = No. of fish in the sample 

r = No. of marked fish recaptured in the sample 

N = estimate of N. 

A A (N-m)(N-c) 

Standard Error (SE)(N) = N T---- 

n~c(N - 1) 

If the, assumption that marked fish are representative of the reminder of the 

population is correct then the only error of estimation are the random errors 

associated with sampling. Experiment conducted at Wastewater Aquaculture Division 

of ClFA demonstrated that Petersen estimator modified by Bailey is efficient for 

estimation of carp population from pond because it demonstrated lower standard 

error, highest precesion coupled with lowest deviation of the estimated population 

from the free population (Roy et. al. 1989). It is funher observed that marking of 

carps by finclipping which can be identified after one year of clipping is suitable for 

batch marking required for estimation of fish population from pond (Roy el el., 1991).

ESTIMATION OF PRODUCTION 

Freshwater aquaculture being subjected to wide range of environmental 

fluctuation passes through various stress condition leading to ,variation in survival, 

growth and production at different point of time. Therefore estimation of fish 

population and production is very important for understanding the process of 

paoduction. In fishery science we are acquainted with the terms like biomass. 

production and yield. Generally no distinction is made between yield and production. 

In case of agricultural crops this may be true. But it is not so, in general, in fishery 

science Biomass is the amount of substance In a population expressed in material 

units, such as live or wet or dry weight etc. It is also termed as standing stock or 

crop. Here we may consider like a wet weight of fish as biomass. Suppose at the 

time of our observalion the estimated number of fish be N with average weight W. 

Then the estimated biomass at the time of our observation is: Biomass (0) = N W. 

Then to express biomass at different periods it is required to introduce time element 

in the above expression as 

Bt = Biomass at time '1' 

N- = No. at lime '1' 

W = Av, weight at time '1' 

S~rnilarly, biomass at lime t1 and t2 can be expressed as 

Produclion in a given time interval is defined (Ivlev) as the total elaboration of 

anlmal tissue during the tlme interval including what is formed by individuals that do not 

survlve lill the end of that lime interval. 

What is produced is production and what is harvested is yield. In fisheries 

the quantity harvested, in other words the final biomass, may be termed as gross 

yield. Net yield is the difference belween final biomass and initial biomass what we 

generally express as production that in reality is yield. That means we never take 

into consideration those fishes who died between initial and final period of growth 

inspite of the fact that Itiose produced flesh during intermediate period. Yield and 

production will be same when there is no mortality during growth period. 

AGE AND GROWTH STUDIES 

The ability to determine the age of a fish is an important tool in fishery biology. 

Simplest and widely used method for age determination is the analysis of size 

frequency distribution. It can be used only to the youngest ege group of a fish

population. During their development fishes pass through several stages each of 

which may have its own length weight relationship due to sex maturity, season, place 

and even time of a day. Hence fitting of regmssion line by least square method in 

each situation is required. For allometically growing fishes, condition fador can be 

worked out to compare individual condition of the fish under varied condition. Since 

growth is a complex procass a complete expression is not feasible, but formation of 

growth models which are basically realistic could be important. For purpose of 

description a number of straight line function, logistic curve, exponential curves have 

been fitted statistically for purpose of evaluation of different curves. The best growth 

model which can be fitted in fisheries production studies is that of Von 

Bertalanffy. This particular growth curve can be used in growth studies of freshwater 

fishes. 

GENETICAL STUDIES 

The foundation of modern theory of breeding are based on genelrcs and 

statistics which together constitute the scientific disc~pline statistical genetics founded 

by Fisher, Wright and Haldane. Therefore there is wide scope of application of 

statistics in fish genetical studies like estimation of genetical correlation, correlated 

response to selection, simultaneous selection of several characters and calculation 

of co-efficients of in breeding and water relationship of various production . 

MODELING OF GROWTH OF FISHES AND POND DYNAMICS 

In aquaculture research, statistical methods used (or establishing of 

empirical relationships are mostly univariate or bi-variate In nature e g I-test. 

correlations, linear regression etc. In many cases one IS to deal w~th several variables. 

as for an example environmental variables as predictor and fish growth as response 

variable; such situation is known as multivariate situation which require treatment 

and analysis of data using multiple regression analysis, path analys~s and 

cannonical correlation analysis. Although manual calculation is very tedious, 

availability of computer and software programs has made these analysis within one's 

reach. 

In a pond environment mullilude of factors interact dynamically and influence 

fish growth and production. Some environmental factors are uncontrollable which 

requires thorough study. Interaction of various factors and their resulting effect on fish 

growth are seldom understood. In order to make the behaviour of these systems more 

predictable on which themselves undergo internal changes over the culture cycle, 

mathematical models capable of describing the fish pond ecosystem practically Is 

necessary.

SAMPLE SURVEYS FOR ESTIMATION OF FISH PRODUCTION FROM INLAND 

SOURCES 

In view of large coastline, multitude of inlad fisheries resources, the diversity of 

fishing practices and scattered distribution of exploiting units it is very difficult to have 

reliable production estimate. Inspite of these, various organisations like IS], NSSO, 

IASRI, CMFRI, CIFRI, etc. during the past decades have conducted pilot surveys to 

standardise the sampling methodologies for estimation of resources and production. 

Presently ClCFRl is running a Central Sector Project entitled Development of Inland 

Fisheries Statistics in India covering various states to develop efficient methodologies 

for accurate estimation of resources or production. This is a potential area of research 

on application of sample survey. Socio economic and technoeconomic surveys to 

assess the impact of aquaculture technology on the society as a whole can be 

studied using suitable sampling methodology. 

REFERENCES 

Roy. A. K., Apurba Ghose and 0.K.Saha (1989) Estimation of some species of fish populations 

from pond by fin clipp~ng and comparative emcacies of three estimators. Envtronmenl & 

ECO~OQY 7(2) : 398 - 403. 

Roy. A. K.. A K. Datta. P R Sen and 8. K.Saha (1891). Preliminary studies on the effect of 

pecloral fiin cl~pp~ng in carps on growth, suw~val and regeneration rate. J. Aqua.Trop. 

e(i991) : 89 - 98 

Roy. A. K, and A. K. Dalta (1995). Two melhods of est~mating Carp Population from closed waler 

bodtes. J. Inland. Fish.Soc. India, 27(1) . 70 - 77.

MANY FACES Of STATISTICS 

INTRODUCTION 

StstktiabavuUy~ldencswithmkn~myofnwtodrand 

technigW8. It plryr a vlW rok in Mi maouch, In industry and -, 

and In 

fofmukning nrUwJ pdld# and prognmw. SWIUo wrrbks tha Id.nthtr lo have 

a full plry for @her mativs palsnthiitbs - to dkcovsr new phenomena wlVIout 

allowing thorn to run rld ad waste in ldvnndng nw concspts. A povrmment an 

pmvkle best bonefib to the poop40 If it takes policy dedsions on the his of a tound 

stathUcd study of problamr. 

How do then laws M theories get establkhd 7 RMm k a lckntlRc method. 

Fimt, a lev is formuleted 8s a prwhlonal hypotherls to explain &ah observed 

evento. S d , tha conseqwms of the hypoVnrk m worked out by mkr of 

Muctlw rsuonlng and vdiW by furthor obuwrtions cdkchd Ulrough unfully 

derigned exFwimOntc. 

If the data contradid the hypothsrls, it h dhclrded, and a fresh one In 

formulsted. Othwwiw, it is pmvirionolly accepted and is given the rtrtus of law - with 

specifled limitation Pnd rcop of applicstions. 

The rcidfic mahcd of investigation krvdving the logical cycle, Hypothesit - 

Data - HypcAhdn, can be achemrticPlly reprersnld as follows :

STATISTICS IN SEARCH OF TRUTH 

A few examples are given to show the inadequacy of measures of location such 

as the average, median and the mode in describing a given population and the pitfalls 

in ~nferences based on them. This is because the individuals in a population usually 

differ substantially from one another and this might make a difference. In such cases, 

we may compute a measure of dispersion (differenms between individuals) to 

supplement the measure of location. Suppose x,. ...., x, are measurements of n 

individuals arranged in increasing order of magnitude. One measure of dispersion is the 

range R=&-XI (the biggest minus the smallest). Another measure is the standard 

deviation S which depends on all the values, where s'=z(&' - x)' + n which is the 

average of the squared deviations of the individuals values from the average x = 

(x,+ ....+ x,)ln. Thus we have two quantities x and s, to describe a population. The 

former measures the general magnitude of values and the latter the spread of values. 

A small value of s indicates more homogenity of the individuals with respect to the 

character under study. 

A single characteristic in a population can be studied easily. Often it is 

necessary to consider two or more characteristics and examine their interrelationships. 

As an example, the average IQ of sons Increases with increase in the IQ of the father. 

This establishes some kind of relationship, though not of a one-to-one type. When the 

values of father) and y(son) ere plotted in their standard deviation units, the slope of 

the regression line as measured by the tangent of the angle i.e. when the slope is zero, 

there is obviously no relationship. The strength of the relationship may be measured by 

the slope of the regression line, which is called the correlation between x 8 y and is 

denoted by r. This can be directly computed from the observed pairs (xl,yl) ....( x,y,) by 

the formula. 

. Relationships between variables are frequently used for predicting one variable 

given the others or controllinp one variable by causing others to take suitably 

determined values. 

The correlation between two variables may be induced entirely by a third 

variable, in which case the observed relationship is spurious and cannot be used for 

prediction. The task of making the necessary computations and updating the 

discriminant function by using fresh evidence provided by concurrent cases and by 

adding newly discovered diagnostic tests is indeed very complex. For this pupme 

modem high speed computers are pressed into se~ce. Computer diagnosis using 

hundreds of measurements is now commonly used in complicated heart diseases.

Mign Of experiments offera a firm basis for dming condmions4rom data. 

Much of the experimental data generated by sdentists go wale or lead to wrong 

condusiona because of lack of adequate antmls and Mas in assignment of Ireatments. 

Most of the quantities involved in fishery research cannot be observed w 

measured throughout the whole population. A section or sample of the whob 

population is therefore examined for attributes concerned (average size or average 

weight) Wch is known as samples. 

In multistage sampling, one does not draw a sample of the desired units directly; 

one reaches such a sample in stages through samples of intermediate units. The 

method can be illustrated in mathematical terms : the population can be split into K 

primary units, each of N individuals, and K primary units are sampled, a subsample of n 

individual being taken from each. 

If m, the mean for the im prlmary unit then the estimate of the mean of any 

sample primary unit 

where xy is the value of the jth individual in the ith unit and the estimate of the 

population mean is 

STATISTICS IN AQUACULTURE RESEARCH 

Dedsion making in aquawlture research presupposes a deep knowledge of the 

aquaculture system and planning the Mure programmes lhrough a well established 

data recording syslem. This is one of the essentials of the farm management and 

deasion must be made as to which parameters, when and how much of them are to be 

monitored. 

Modelling and optimization of growth of fish in aquaculture is very important 

factor of study for the success of the operation of Ule aquauclture projects. 

Management of ponds is based largely on monitoring complex processes of pond 

dynemica and sansWty to environmental and operational factors. Physiological and 

biological parameten over a long period of time makes certain demand6 for a large 

storage capacity of computer for developing data acquisnion system.

For povldlnO etmbgk data for pEwJnp md rubHquwd Ink of handling 

Uwough Hltomrtkn, cmpttn-ddd design, computer-rldsd imbmmbtkn and data 

trwmlukn,#dogicd~lnrbvnrnbtknr(c.urranofth.- 

~krwhlch~eopsrrtrdtoq~murdrthrwghth.t#lpofmicro 

comwn. 

Theurrofcomputsrmoddrh~msydrmpl.yrur~portmtmkt0 

obtskrbetteruds~ofthepcdeco8yrtsmmdckwkpm~~ 

t ~ o p t k n k s ~ ~ ~ . I t h n g O t ~ i r n p o d f w ~ a i t i c 

parameter8 that haw hbhef docfa on pond's pro- and hence Lh produdkn. 

Multivarktr adyeb for devdoplnp modrk In the bmnch of rt.thtko cormrrted with 

analyshg muklpk rn+rurcmnntr thlt have boon msde on wen1 samples of 

Individuals. VarkUer ue dependent among themadver so that m an not split off one 

or more from others. CompuMional malysis lncorponthrg a large number of variables 

h the only 8oluUon to arrhre at the conduelon IdonWykrg the ulticrl parameten. 

In order to cbvdop effldsnt and ownomicll feed fonnutn for aquaculture, the 

basic information b nqulred on nutfbnt mquhwb of the species cuttlvatd, the 

chemical comporltlon md oqMokptlc propsrtier of feed hgredhnb in relotton to their 

acteptabU#y Md &lHy of fhh to dmt and utiW nubienh from va&us sources. 

Linear programming k a mrttwtnaUcrl technlqw bawd on matrix algebra and best 

suned to a computer. Thh offem mnrldenbk pdmtlal h the development of 'Least 

colt IW tonnul;llkn of flrh dkts'. 

Recad keeping dated to brood stock management wwM be better performed 

on a microcomputer. Gomtkte would like to incorporate new record rarity into a 

contlnuoru dotabom wMch am doveloped gently In drt.bns management 

pacJwge8 Hk dBm IV, Foxbus, R&X md Foxpro ate. The support ohfed by the 

modem computer wfWm techndoOy h th fkld of gcwtkr, Wng from 

chmmosomo wlyrh to genu nupplng or DNA 8epmchg math to hasten the 

pmgmss In gene technoiogy. Qana t.chndoly suppwted by th computer Whnology 

has a much more gmter rda to play in quadtum mwarch. 

The growing sdem of aquatic mlaobidogy wkh reference to aquatic 

producttvky, organic decornporltion, tbktkation, MoRnention and other biotic 

approaches to improw prodvdMty ha8 been bm&ting lnwwnnly from the 

Molnfomutia bawd on computer epplldon. 

Work on dewbpmonl of m(w qudlty mod.ls, -1 modoh, phyrlal 

modeh, economic modela etc. am 8una of tho mrthmrticd mpmmWbm whlch can 

be derived empidcalty or macluniaticaffy. The m o m , ldsnURcrtion of c~ntrd 

procesrer md facMas pmvick r map opportunity Md m exciting ch.lhge for 

sgu-.

lnvesm requirement for aquaculture, pmfdr, ntcr of mkm, growth nte, W 

requirement, mortality, dwng density, incidence of diiase in culture operations etc. 

are analysed through computers by a krga set of built-in mathematical and statistiul 

functions developed in programming languages. 

CONCLUSION 

Statistics proves a necessity when researchen contemplate advanced study 

with the objects of doing research. Statistical principles are involved in the effident and 

economic design of experiments as well as in the interpretation of the results. 

Appli~tion of statistics is modem mode of interpretation of scisnttfic data and drawing a 

right conclusions eliminating probabilitiie and posribilit'is.

FUNDAMENTALS OF SAMPLING AND ITS APPUCATION IN 

FISHERIES RESOURCE ESTIMATION 

S. Chakraborty 

Deputy D~rector ol Fisheries 

God. of West Bengel 

Some basic sampling concepts and basic sampling techniques 

Population : It is defined as the collection or an aggregate of all possible values of a 

particular characteristics for a specified group of individual. 

Example: i) populat~on of f~sh weights of all fishes In a pond. 

~i) population of income of f~shermen fam~l~es in a State. 

iii) population of fish length in a sea. 

A population can be finite or infinite. It is said to be finite if it contains finite no. 

of ind~viduals or un~ts. Example (i) and (ii) given above refer to finite population. A 

population of unl~m~ted or very large measurable no of individuals is called infinite 

populat~on. Example (ii~) above refers to infinite population. The no. of individuals or 

observation is called populal~on slze and usually denoted by 'N'. 

Sample : A group of individuals or units that is chosen from a population is called a 

sample. The no. of ind~viduals or observations in a sample is called sample size and is 

generally denoted by 'n'. 

Sompllng frame : It is a list, map or other specification of units which constitute 

available information regarding population. It forms the basis for drawing of sample. 

Random sampling : A random sampling is a method of sampling in which each 

individual in a population has a preassigned chance of being included in the sample. 

Generally units are drawn one by one from the population. If the chance of 

selecting any unit at any drawal is the same then the sampling is called the simple 

random sampling. S~mpte random sampling can be obtained either by using 'Lottery' 

melhod or by the use of 'Random Number Tables'. 

Loffery method In this method, first number the individual of the population. Then 

write these numbers on identical chits and fold them so that the nos. are not visible. 

Then place lhese chits in a box. Shake the box thoroughly and draw chits one by one 

t~ll the no. of chits drawn equals to the sample size. Note down the nos. of those chits. 

The individuals with these nos. form a sample.

Use ofnndom numlran : Prepared tables of random nos. are nvaiiabba lor drawirig a 

rbnple random sample. These tables consist of series of digits fmm 1 lo 9 which 

appear Indewndent of each other and appear approximately aqua no, of times. 

As a first step, units Of the population are numbered from say 1 to N. From 

random no. tables, select a no. between 1 to N and include the unit bearing this no. in 

the sample. Continue this process till the no. of units included in the sample equals to 

the sample size. In this procedure nos. larger than N are not considered. To avoid 

rejection of such nos. 'Reminder approach' methods a n adopted which is described 

below. 

if N is a 'd' digit& no. determine first the highest 'd digited multiple of N. Let 

this be 'N'. Then a random no. 'r' is selected from 1 to N. Divide this selected 'r' by N 

and find out the reminder. A unit with serial no. equal to this reminder is se\ected. If 

the reminder is zero, the last unit (N) is selected. 

Example:- If N = 20, the highest 2 digited multiple of 20 is 80. Then select a random 

no. from 1 to 80. Let this no. be 72. Division of this no. by 20 glves a reminder of 12 . 

Hence, the unit with serial no.12 is included in the sample. Select another no. from 1 to 

80 and repeat the procedure till the no. of units selected equals the sample size. 

A sample survey is a vehicle for inductive reasoning. It provides for the 

transformation of observations of a part into conclusion regarding the whole. Taking 

samples is a procedure used in nearly all fisheries investigations and from the sample 

taken we intend to generalise about populat~on under investigations. For example, 

taking a sample of catch from a vessel operated in a water body. We want to say 

something about the total catch of fish from it. 

The basic sampling techniques are 

i) Simple Random sampling 

ii) Stratified sampling 

iii) Cluster sampling 

iv) Systematic sampling 

v) Two stage sampling 

In this mmpling all unib have equal probability of being seleded in the sample 

and wsry possibk sample of required size has the same chance of selection. The 

mmpk is drawn sither by lottery method or by random number table.

In stratifmd random sampling, the population b divided into m over lapping 

sub-populations called strata. A sample is then drawn from each stratum. The prime 

reasons for stratification are - (i) It ensure adequate representation to various sub 

division of the population. (ii) It may be convenient to break up the populetion into 

strata for better organization end supervision of field work (iii) A considerable 

precession may k gained by dividing a heterogeneous population to homogenous 

strata. 

In cluster sampling the population is divided into groups or dusters of units. 

Several of the cluster8 a n chosen at random and all units in each selected cluster 

become part of the sample. The choice of cluster sampling in fish catch surveys is of 

immense use. 

In thin rampling fmt we relea clusters, called 1st stage units and then chosen 

units called 2nd stage units from the dusters. For example, in estimating the yield of 

fish In 8 distrid, village may be considsrd as let stage unit and the ponds within 

vlllage as 2nd stage unit. 

In systematic sampling. the first unit is selected at random, the rest being 

selected according to a predetermined interval. In estimating marine landings or 

rlverine landing, the systematic sampling technique is normally used. 

Reliable and sound data base is a prerequisite for proper planning and 

management of inland fisheries. At present, the available data base on inland fishery 

resources and their exploitation is inadequate and a10 suffers from various drawbacks 

due to coverage, classification and methodology of collection of fishery data and its 

estimation procedure. The statistical methodology which may be applied in various 

Inland fishery resources as described below may provide reliable estimates on 

resources as well as production. Inland fisheries ere broadly classifred into capture and 

culture fisheries, the format being expioitive of natural population and the catch king 

intensive intervention of human by stock control and management practices. The 

culture fishery resources are pondskanks (impwnded water bodies), Ox-bow 

lakes/Beel and Baon, Brackish water fisheries, Reservoirs and Rivers, Estuaries and 

Lagoons are the capture fishery resources.

Presently. area approach is being followed for estimation of inland fish catch by 

using 'Acreage' and 'Yield rate' data as available through sample sutvey. The area 

under different culture, inland fishery resources may be developed in the following 

manner. 

Type of resources 

Source of area data 

1. Pondflanks ( impounded 1. CuHurable water area may be developed on the 

water bodies) 

basis of complete enumeration or through 

sample survey based on sampling 

methodology. 

2. Ox-bow IakesBeel and Baors 2. Through settlement records 

3. Reservoir fisheries 3. Through 1 8 W Department of respective State 

4. Brackish water fisheries 4. Through complete enumeration and also 

through implementation of Fish producers 

licens~ng order. 

ESTIMATION OF PRODUCTlVlTYlCATCH FROM IMPOUNDED WATER 

RESOURCES 

Impounded water bodies viz.. ponds and tanks contribute appreciable to the 

total inland fish production and the assessment of its contributions are being prepared 

on the basis of sound sampl~ng technique. The sampling technique and the estimation 

procedure described below provide precise and reliable estimate of productivity and It8 

fish production. 

For estimating the fish catch from these resources, a stale may be divided into 

three Agro-climatic zones. The criteria for classification adopted here is on the basis of 

high, moderate and low ra~nfall, temperature and soil type etc. 

From the high rainfall region, a set of three districts are selected at random for 

catch estimation where two districts are selected from moderate rainfall area and one 

district from low rainfall area in order to provide larger sample for high concenlration of 

units and smaller sample for low concentration of water units. Here, it is assumed that 

these sample districts represent the districts from which they are selected. 

The sampling design for estimating the productivitylproduction under these 

resources are stratified three stage cluster sampling. A district ie divided into three 

strata approximately of equal sue in respect of water arednumber of villages. A 

sample of six dusten, of five villages each are ~lected from each stratum. Cluster of 

villages constitute lhe first stage unit and the ponds within cluster as the second slag.

unit. Selected villages are ourveyed completely and all the water unb in the village are 

enumerated. 

The selection of samples are prepared by adopting the following procedure. 

List all the villages in a district. Now the district is divided in 3 strata such that the 

number of villager in each Stratum are approximately equal. From each stratum, six 

villages are selected called the key village at random from the list of villages. Then 

l~sting of ell the villages surrounding each of the key village are prepared. From this list 

4 villages corresponding to each of the key village are selected randomly. In this way a 

sample of six clusters of five villages each in a stratum are selected for resource 

estimation. 

For estimating the total catch of fish, five pondsltanks are selected from each 

cluster at random from the total number of ponds in the cluster. In case the number of 

ponds in a cluster is less than 5, all are taken in the sample for observation of catch. 

Thus, from each district a total of 90 villages are selected for est~maling the water area 

under ponds and tanks and 00 ponds for estimating the catchlproductivity of fish. 

Further, sampling In time are adopted so that each water unit is visited at least once in 

e month by an investigator for record~ng the catch from each pond more accurately and 

for prov~ding the est~mates of monthly catches also. 

Estimation Procrdurr 

Nh = Total number of clusters in h-th stratum 

n, = Number of sample clusters in h-th stratum 

MW = NO. of ponds in the J-th village of i-th cluster in h-th stratum. 

my, = No. of ponds selection from i-th cluster in j-th stratum 

Xyl = Total area under water unit In the j-th village of i-th cluster in the h-th stratum 

xkh = Area of the k-th selected pond in the i-th cluster of h-th stratum. 

Ykh = Yield of k-th selected pond In the i-th cluster of h-th stratum. 

= Average yield per cluster in h-th stratum 

)'* = Average yield per hectare per year in h-th stratum 

Yh 

Estimators of area and Number of ponds 

Average number of ponds per cluster in h-th stratum 

Total no. of ponds in the district is given by M = N, M*

1 

Average area per cluster In h-th stratum s -: = - ,Y,,,vherrX., = 

!Ik 

,Y# 

A', 

Total area in the district is X = N, 

x; 

E~tirnatora of yield 

Average yield per cluster in h-th stratum 

- 1 

Where qh = - 1 Y* 

m, 

Total y~eld in the d~strict ( Y ) 

Average yield per hectare in the d~strict 

ESTIMATION OF FISH CATCH FROM CAPTURE FISHERY RESOURCES 

Under this resource Rivers, Streams, Estuaries etc constitute one of the 

important inland fishery resource in the State spreadin0 over thousands of kilometers 

and passing through mountains, valleys, pla~ns and other areas An appreciable 

quantity of fish are being landed from these resources. The estimates of its 

contribution are being prepared based on sound statistical technique and the procedure 

described below provide reliable est~mate of fish production 

Sampling Design, methods of data collection and estimation procodure 

Capture fishery resources under rivers, streams etc, sustain mult~gear and 

multispecies fishery exploited by art~sonal f~shermen operating on the area of the 

system. Most of the rivers have well established landing centres where fishermen land 

their calch. From the landing centres data on fish catch etc, are collected by the held 

i ;vestigators.

The sampling design adopted h a two atage stratfkd sampli involving 

stratification in space and lime viz., landing wntrea and days reqedvely. 

The entks stretch is divided into homogenous zone of landing mntre each zone 

having more or less same type of gear and craft, flshing practices and species landed. 

From each zone D few landinp (20%) centres are randomty selected. A month is 

divided into three sets of ten co~eartive days. From the first set, two consecutive 

days aw randomly selected whom olmvatlons are taken from the se\ected centre. 

From the ~ ~ and nthird set d of ten days each dusters of two days are taken with a 

sample interval of ten days. 

On the selected first day of observation in a landing centre, data are collected 

during 12.00 to 18.00 hm. and on the second day during 6.00 to 12.00 hrs. Data on 

night landing if any in between thew consecutive days are collected by enquiry on the 

second day. Thus in two day duster 24 houn observation is taken. This forms a 

landing centre day the fimt stage sampling unit. On the selected day of observation, if 

number of units landed is 10 or less, then all the units am observed for gear wise 

catcher. When it exceeds ten a sample of units not less than ten is selected in a 

syrtematic way depending on the total number of units landed during the period of 

observation. Units landed form the second stage sampling unit from which data on 

specisswise catch, type of crafl and gear operated are collected. 

Estimation Procadun 

Let n sample centres are selected from a population of N and let d no. of 

sampling days. 

D, = number of Fishing days at i-th centre in a month 

Y1 = Catch of I-th landing centre on j-th selected day. 

1 

71 = ~ ean yield of the i-th centre = -x~ 

d 

N - 

Then Y = Estimate of total yleld from all the centre = TZ D, y,

The data given below relate lo three dusters of stratum-l in ths district of 

Minapore, West Bengai for estimating the total area under ponds and tank. Tha tobl 

no, of dusten in the stratum in 349. The sampling methodology ir 8tratMed duster 

rampling. 

Cluster SI. No. of Village No. of Ponds , Total Area 

Compilation Procedure: 

Total catch for 20 ponds in cluster - 1 = XY,,, 

Average catch/Pond in Cluster - 1 = 

11= 2 m I 

= 1867.5120 = 03.47 

Average cetch/pond in cluster - 2 = 1189.5113 = 91.50 

Average catcNpond in cluster - 3 = 1729 0112 = 144.48

A 1 

Average catch per cluster = - = - (2617.3 + 2836.6 + 3313.9) 

Y, 3 

The following data relate to the estimate of area and variance in four strata of 

Midnapore district In the state of West Bengal for estimating the total area under ponds 

and tanks. 

- - 

Stratum Nh nh A A 

Ah 

MA 

(no. of pond 

in village) 

IV 634 3 0.1261 0.00134388 98.67 

Compilation Procedure: 

Total No. of Ponds = M = XSN, A 

M,

Average area per pmd = = N. 

A 

Total area = number ponds x average area per pond 

= 491581 x . 1722 

= 84650.25 (ha) 

The data given below are from one stratum of the district of Midnapore in West 

Bengal for estimating the catch. The total no. of clusters in the stratum is 349. The 

sampling procedure is two stage stratified cluster sampling. 

Stratum C, 1

Compilation Promdun : 

We prepare (he following table 

Stratum Clulter SI. No. of Village Av, area per pond 

1 1 1 0.1200 

2 0.2987 

3 0.6180 

4 0.3740 

5 0.2750 

.-.- 

I I: , 

A, N, A,, A,, 

is average area of pond in i-th duster 

Estimated varlance of A. , ; (2) 

A, A, 

= (--- 

A 

Tatel catch kt stratum - 1 = Y = Nf -i;; 

A A 

A, A, 

Flmt the yiekVhectan for each pond la calculated

Average ykM per hectare for duster - $ 

Average yieM for duster -2 

Average yieM for duster - 3 

Average yieldlhectare in stratum-l

Sa~npling Techniques Applied in Assessing Inland Fishery Resources 

and Production 

R A. Gupta 

Cc~ttrnl lttln~td Capture Fisheries Racnrcl~ Institute 

llnrrackpore 743 101, West Bengal 

India is er~dowed wit11 very rich and polenlial inland fishery resources 

Tltcse resources t~eed to be judiciously exploited and managed in order to get sustainable 

yields OI long tenn basis The decision makers need reliable data not only to assess tlie 

levels of exploitation of these resources but also sucll data are needed for planning and 

for~nulation ofour future strategied for balanced development of inland fislieries This 

is in this respect tliat we need sampling niell~odologies which may l~elp to assess these 

resources in ternls of area of coverage and productio~i of fish from them Tlie nature, 

nu~nber aid tyl)e of inland water bodies yielding fislt are so luge and diverse \hat it 

seelils ~ ~~~eco~~o~~iical 

to adopt nny type ofnielliods e~nployitig total enu~neration and 

l~e~~ce justifies adoption of saniplirig rr~etl~odologies for their assessnlent The present 

lecture deals witli the san~pling neth hods nlost appropriate for assessment of inland 

fisliery resources and production. 

Before I embark upon the discussior~ on the sanipling tecliniques used for 

fisheries assessment 1 feel it necessary to enlist the types of resources used for inland 

fisl~erics and ndopt soti~c acceptable c~iterion for tlieir classification depending on the 

modes a~id nature ofexploitation of different classes 

Clnssificntiott of i~ilil~td Iislleries resource 

A 111ajor bottleneck encountered ir~ 

data collection refers to anibiguity in the use 

or concepts and tcrtninologies in definition, nomenclature and classification of the 

diverse Iialure of resource in dinerent states and union territories To overcome this 

deficie~lcy a complete framework ofconcepts have been formulated on the basis of pilot 

studies conducted ill various agro-cliniatic regions of [lie country in order to bring ill 

unifor~irity nt tlie national level Inland fishery resources can be described in tlie 

following Ir1anner. 

A, Frcslt ~vntc resources sucl~ ns :

I. Aquaculture ponds and tanks 3 Playas 

2. large irrigation tanks 4. Waterlogged 

5. Rivers and canals 9. Quarries 

6. Ox-bow lakdcut-off meanders 10. Ash ponds 

7. Reservoirs I I. Excavations 

8. Swamps 

B. Saline water 

1. Lagoons 

2. Estuaries 

3. Creeks 

4. Mangroves 

5 Salt pans 

6 Marshes 

7 Other impoundments ( Bherries etc ) 

Many of the water bodies mentioned above contribute very marginally to the 

total fish production and hence may not be of much in~portarice in formulating s~rategies 

the purpose of production assessment Hencc all those potential clnss of water bodies 

need coverage under catch assessment prograriirnes are being classified below for the 

execution of the metliodology in order to provide firni, reliable and statistically sound 

data base on inland fislieries. 

Group -1 : (Water bodies up to 10 ha water spread area at full rank level) 

I. Aquaculture ponds and tanks 

2. Brackish water impoundments 

3. Waterlogged areas 

Group U : 

I. Large Irrigation Tanks 

2. Reservoirs and check dams 

3. Lakes and Ox-bow lakes 

Group 111 

1. Rivers 

2. Canals 

3. Estuaries 

4. Lagoons 

5. Back waters

Separate sampling methods have bem devised fw estimation of resource area, 

fi~h production md other parameters of imponmw. 

Sampling Procedure lor Croup 1 water bodiu: 

Ponds and Tanks : Stratified three stages sampling design (Cochran, 1962, 

Sukhatme CI al, 1984 and Gupta et al. 1997) is adopted for assessment of water spread 

area and fish production. The entire state is divided into three nearly homogenous 

groups called strata keeping in view certain characteristic such as rainfall or soil 

conditions. Strata should be formed in such a way that geographical contiguity of 

districts within the strata is maintained. Districts from each stratum forms first stage unit 

of selection, clusters of five pond bearing villages form second stage unit of selection 

and ponds within clusters as the tlird stage unit of selection. The ultimate unit is selected 

in the following manner. 

A sample of 2W of the districts are to be selected from each stratum subject to 

a minimum of two districts are included in the sample within each stratum. A list of 

villages bearing ponds and tanks is then prepared and clusters of five villages are formed 

for further selection. A sample of 10% of the clusters ( 2nd stage) is selected from each 

sample district for estimation of pond area statistics. At the third stage of sampling five 

ponds within each selected cluster is taken by simple random sampling for estimation of 

catch. However, locations, where units are widely scattered and formation of cluster is 

not beneficial, may adopt simple random sampling. 

Notations: 

Let 

N, - Nuniber of districts in h-111 stratum 

4 = Number of districts selected in h-th stratum 

M, - Number of clusters in i-th district 

m, = Nuinber of clusters selected in i-th district 

N, 

M~=C Mh, 

L.1 

=Total clusters in h-th stratum 

Bu - Total number of ponds in j-th cluster of i-th district 

Blyi Number of ponds harvested in j-111 cluster of i-th district 

bw Number of ponds selected in j-th cluster of i-th district 

%= Area of k-th pond in j-th cluster of i-th district 

& = Area of all waterbodies ill j-th cluster of i-th district

Ny = Area of dl waterbodies harvested in j-th cluster of i-th district 

(a) Estimation of total area ( Two stage sampling) 

fitimale of average area per C ~USK~ 

"h 

EM,, 4, 

; where ii,,,=-x A,, 

= - 1.1 I m* 

"" ,#& mhrl-1 . ... . ,, ., (1) 

fitimate of average area harvested per cluster 

"h 

x M*, 

z;, 

I "" 

; where

,vh, b, 

I U Bh4wb=- Mk 

Bh ll*,., mk/'l M, 

c =-C I ' 

- ; where - 

b,=-C 

rsrrmate of average ptrdr hamsred per clus~er 

I=-c w, Fir' ; where 6;-c B;~ 

c I "b -, 1 "' 

Bh 1lhl., "'~1'1 

Estinrate ojtoral p~rdc it1 h-th strarunl 

b 

" d 

nh = B~*M,, ; ~vlrrrr Mh, = 1 Mh, 

1.1 

firintate o/ toralpttdr lrarvesred itr 11-11, stratum 

1 I 

13: = B-,'*Mh, ............ (8) 

3. Estimation of fisl~ yield (Three stage sampling) : 

Let 

yw = Yield of k-th ponds in j-th cluster of i-th district in h th straum 

xw a Area of k-th pond in j-th cluster of i-th district in h-th stratum 

brinrate oj yield per prrd irr j -th cluster IS 

.... 

hlirtra!e oj yield per clt~srer F i-rh d~srrict is 

Fh, = Lz B;&, 

hr 

&rin~a/r oj yield per clrrster F h-tlr srratr~nr is 

A_ 

" I 

...... 

Y*=-C lVk Yh, ........ 

'I4 

.(lo) 

..(I I)

Similarly esrimarejor area bused on seleoedpoirds IS, 

Esrimaie of area per cluster is 

The above estimates assume that MI'S and Bb's for the populatiori are known 

heldper hectare (Ratio brimare) 

A_ A- 

i=( Yh)4 Xh) 

Esiinrare of rotalyieWjronr h-rh srraruni based or1 (he ratio rslinrate is 

* 

(F,)=i A; or (y,)=i ,iL 

here ~L=rocal area harvested ulrder yotrh atrd io!iks i/r rlre sira~itnr 

This nray be replaced by A: 

The above esrmiare is rjlicieiir bur biased. 7he bra.$ tvill he rit~gligrhle 

Sampling Procedure for Group II wnter bodies: 

Raervoirs, Irks, beelr and large irrigatio~~ tat~ks: There is a great variability 

with respect to size and productivity of various reservoirs in India Hence, there is a 

strong case of sub classify them into various subgroups on the basis of area in order 10 

make reliable and accurate assessment of fish production. The following subclassfication 

seems appropriate 

Small reservoirs (I0 to 500 ha of water area at FKL)

Medium reservoirs (500 to I000 ha. of water area at FRL) 

Large reservoirs (1000 ha. and above) 

As far as area statistics is concerned. a Iota1 inventory of resources under each 

stratum is made and then the following selection procedure is adopted for estimation of 

fish production. 

The water bodies under each stratum are classified into the above three groups 

and a random sample of 20% of the water bodies from each group may be taken for 

survey for physical observations. Further classification on the basis of information 

available on the type oftheir exploitationd may also be made. For making strategies for 

collection of catch da~a on harvesting days this type of classification would be 

advantageous. Therefore, it may be suggested that they may be sub-grouped into the 

following two categories. 

1. Waterbodies which are harvested during a short interval extending from a 

fortnight to about a ~nontll. These water bodies are mostly small reservoirs and lakes 

which fall under the perview of state departments and exploitatin is affected either by 

auctioning them to private contractors under certain terms and conditions or exploited 

depart~nentally by engaging contract labour Hence, the bulk of harvest is a one time 

operation which continues for a fortnight to about a month Data on catch of 20% of 

such water bodies selected by simple random sampling should be observed by the survey 

staff through pl~ysical observation to cross check the authenticity of the catch records 

maintained by the agency. 

2. Water bodies which are exploited round the year by fishermen cooperatives of 

individual fishermen on the basis of licenses, free fishing, royalty or any other such 

mode. Selection of 20% of water bodies in each stratunl is made by simple random 

sampling procedure. Assessrilent of catch is undertaken for selected water bodies in each 

stroluln by adopting sa~npling villages as the second stage unit of selection. Each 

sampled village is then observed as per the scheme suggested for group 111 for recording 

the data on catch. 

Notntiot~s 

Let 

N, = Total Number ofwater bodies of the 1-th sub-group in h-th stratum 

N,' = Number of water bodies harvested in h-th stratum 

n, = Number of water bodies selected from N, 

n,' = Number of selected water bodies which have been harvested among n, 

q,,,= Area ofj-111 water body ofthe I-tti sub-group in 11-111 stratum 

yhu = yield of 1-th water body ofa group in 11-111 stratum

(Value of yw is obtained by recording total fish catch in cases where water body is 

harvested during a short interval of the year However, water bodies which are harvested 

during the entire yea as discussed in the sampling procedure, y,,,, is estimated by fbrther 

sampling as under) 

(1) U total fish catch is recorded at a centre on each sanlpling day : 

Average caich ar k-ill cerrtre per dzy 

1 1 

Yh,,k=qC Mh#,k, .G,kl wht,re h#l = -C) hfl,#, 

ht,k ' "lhlill 

b/rmaie oj average carclr at k-th crrrrrr cltrrr~~g rhc nrot~rlil~~ar 

brtma/e of 

'hyk 

roral caich J - lh water bodp 

(1 5) 

(16) 

where 

y,,,, = yield of I-th day of k-111 centre at j-111 water body of I-111 sub-group 

DM, = Total fishing days in the k-th centre ofj-th water body during the montNyear 

d,,,, = sample days selected out of D, during the mont Wyear 

(MontMyear will depend on whelher estimates are prepared n~onthly or yearly) 

Mkuk, = Total nets operated on I-th day of k-th centre at j-th water body of I-th subgroup 

~s, = Total nets sampled on I-th day ofk-th centre at j-th water body of I-th sub-group 

(2) Iffish catch is recorded by observing further sampling of few gears out of the total 

gears used on the sampling day 

Average yieldper srlec/ed waler bdy ojl-ih srrb-bwrrp 111 h-111 slrclrrmt 

Similarly, average area jwr Haler 

is

fitinrate oj yieldper hoctare (Ratio estimate) 

Estimate of total yield is (on the basis of total l~arvested nrea) 

L/~IIIu/~ r,//o/aljis/r pdrrctio~l for /Ire slale rr~rdcr GI orcp-lJ is give11 by 

San~plilig Procedure for Group - 111 water bodies: 

Stratifed two stage sampling is adopted for this group A list of fishing villages 

is prepared before hand and then a simple random sample of 20% of the villages froni 

each group is selected for observation of catcli by the following procedure. 

Each selected centrdfishing village is physically observed on two consecutive 

days in each of the first and second fortnight during the month. On a selected day of 

miipling at a centre, data is collected during l2OO to 1800 hrs. and on second day from 

0600 to 1200 hrs. Data on night landings, if any, in between the consecutive days are 

collected by inquiry on the second day. The information should be collected from the 

fisllernlen by both enquiry and physical obsetvation. On the second day of observation 

the investigator should collect inforniation on the total number of fishing units operated 

on that day, fishing tunits sampled out of the total, the total catcli landed from the 

observed units and species composition. He should also ascetiain the number dof fishing 

holidays by esch type of fishing units since the last sarrlpling day However, the san~plir~g 

days in a nronth may be increased depending on tlre available resources and the units 

potential ill fish landings. 

N, Nurnber of landing centredfishing villages in 11-111 stratum (h=1,2,3) 

n, = Number of landing centredfishing villages selected in 11-th stratuni 

G, =Types of nctdgears used in i-th village 

D = Number of lishing days during the month ofj-111 type

stratum @I,2 ....... G, ; i=1.2, ........ N,) 

dGj = Number of sample days during the month ofj-th type net in i-th village of ti-th 

stratum (j=1,2 ....... G, ; i=1,2, ........ n,) 

Mw = Number ofj-th type net operated on k-th day in i-th village of h-th stratum 

= Number ofj-th type net observed on k-th day in i-th village of h-th stratum 

Yw - Fishing yield ofeach unit ofj-th type net on k-th day in i-th village of h-th stratum 

Average caichper wrrl ('~eU~rei-irde) 

firrmare of average corch oJ/-lh rjpe rrei pBr duy 

dhv 

j 

h,, hl,k 

filinrale of average caiclr per cerrire 

Total motrlhly ca~ch ill h-ih sirarunr i.r

Reference 

I .Cochran,W C..1962.Sampling Techniques. Willey Eastern Limited, New Delhi & 

Bangalore. 

2.Sukhatme.P.V. Sukhrtme,B.V.. Sukhatme.S and As0k.C. 1984.Sampling Theory 

of Surveys with applications. lowa State University Press, Ames. lowa 

(USA) and Indian Society of Agricultural Statistics, New Delhi. 

3.Gupta. R.A.. Manda1.S.K. and Maumdar,S., 1997.Methods of Collection of Inland 

Fisheries Statistics in 1ndia.Central Inland Capture Fisheries Researcl~ 

Institute, Barrackpore. Bull.No.77.

CORRELATIONS ANL) REGRESSIONS 

A.V. Surly* Rao 

Cenrrd Rice Research lmtrruta 

Cnrtack 

CORRELATION 

When information on two or more variables are processed. ~t is natural to think 

whether any functional relations exist among these variables. If any functional 

relationship exists among variables, then a question comes to our mind that how closely 

are the variables associated In other words, we seek the degree of association among 

the variables. 

The techniques, developed to measure the degree of association among 

variables, are known as correlelion methods and when an analysis is performed to 

determine the amount of correlation with its level of significance, it is known as 

coneletion analysis. The resulting measures of correlat~on are known es correlation 

coefficients end it is denoted as r (for simple lineer cofrelelion between two veriebles). 

When more than two variables occur, the correlation coefficlent is denoted as R and is 

known as multiple correlation coefficient 

Formula for computation of simple hear correlation coefficlent r between two 

variables, say, X and Y is given by: 

r = Cov(X,Y)/ Sqri {(Var X) (Var Y)) I e r = Z xy / d{ (~x')(~y')) 

X W n and i' = XYln 

The value of correlation coefficient lies between -1 to +I and it has no unit. 

When the value of the correlation coefficient is equal to 0, we say that there is no linear 

association between the variables. On the other hand, if the correlation coeffic~ent is 

equal to -1, we say that the two variables are negatively associated which means that, 

when a positive change in one variable is associated with a negative change In the 

other, and when the value of the conelatim coeRctent is +I, it is positively associated 

indicating there by that, both the variables changes in the same direction. 

Even though the value of correlation coefficient is zero, it does not indicate the 

absence of any relationship between two variables It is possible for the two variables to 

have a nonlinear relationship. This is the reason why it is preferred lo use the word 

linear in simple comlation coefficient, instead of correlation coefficient.

Test of significance of the simple linear correlation coefficient by comparing the 

computed r value with the tabular r value at n-2 degrees of freedom, where n stands for 

the number of observations with which !he computation is performed. The simple linear 

correlation coefficient r is declared to be significant at (say) a level of significance if the 

absolute value of the computed r value is greater than the tabular r value at the a level 

of significance at n-2 degrees of freedom. The term significance is generally to know 

whether \he linear correlation coefficient r is different from zero. 

In case of more than two variables, the linear correlation coefficient is known as 

multiple correlation coefficient and is designated as R. The significance of R is 

assessed by F-test with n-p-I degrees of freedom, where p is the number of 

independent variables under study. 

Closely related to multiple correlation is that of partial correlation. By partial 

correlation we mean that the correlation between two variables in a multivariable 

problem with a restriction that any common association with the remaining variables has 

been eliminated. For example, a first order partial correlation coefficient is one which 

measures the degree of linear association between two variables after taking into 

account thelr common association with a third variable. 

If there are three variables say 1, 2 and 3, we can have three simple linear 

correlation coefficients i.e. r12, r, and r,. The partial correlation coefficient between two 

variables, sey 1 and 2 when the third variable 3 is held constant, i.e. taking into account 

the common association with the variable 3. Symbolically, we write this as: 

The partial correlation between two variables when the third is held constant, is 

also known as first order partial correlation coefficient . Similarly the second order partial 

correlation coefficient can be, symbolically , written as 

which measures the association ship between variables 1 and 2 independent of the 

variables 3 and 4 

REGRESSION 

When two or more variables are related to each other, we not only seek a 

mathematical function which tells us how the variables are associated, but also we seek

to know how precisely the value of one variable can be predicted if we know the 

value(s) of the assoclated variable(s). The techniq~s us4 to eccompllsh these 

objectives are known as regression methods. Regresston methods are used to 

determine the best functional relation among the variables. 

Regression procedures can be classified according as per number of variables 

involved and the form of functional relationship between the dependent and 

independent variables. The procedure is termed simple if only two variables (one 

independent and one dependent variable) are involved. In case of more than two 

variables the procedure is called as multiple. If the relationship is hear then it is termed 

as linear, otherwise nonlinear. Thus the regression analys~s can be classified into four 

types as follows. 

Simple linear regression 

Multiple linear regression 

* Simple nonlinear regression and 

Multiple nonlinear regression 

LINEAR REGRESSION 

For simple linear regression analysls to be applicable, the followtng conditions 

must hold: 

There is only one independent variable, (denoted as X) affecting the dependent 

variable ( denoted as Y) 

The relationship between Y and Xis known, or can be assumed, to be Itnear. 

To compute the regression equation, it is required to estimate the regression 

coefficient b and the intercept (constant) a for which it is required to assume one 

variable as dependent and the other as an independent As a general practice, the 

variable designated as X is an independent and the variable designated as Y is a 

dependent on X are assumed. Regression coeffic~enl b is then estimated as: 

Estimation b, the regression coefficient and Ihe intercept a may be computed as 

follows:

z XY 

b = - and a = P - b 2 where the notations have their usual meanings. 

For testing the significance of b, t-test is employed. The test of significance of b 

ie done to examine whether or not the coefficient b is different from zero. Since !-test is 

based on the normal distribution; it is necessary that the variable X must be normally 

distributed observed samples. Generalized I-test is given by: 

difference 

t = ---------------- 

standard error of their difference 

The standard error (denoted as 5,) 

b is given by: 

For testing whether or not the intercept a is different from zero (the regression 

line passing through the origin), the formula is given by: 

Although the assumption of a ltnear relationship between any two variables in 

biological materiels seldom holds, it is usually adequate within a relatively small range 

in the values of independent variable. For example, the growth rate (as measured by 

weight or size) of living indtviduais is rapid at younger age and remain static or declines 

considerably as the individuals become older. 

The relationship between any two variables is linear if the change is constant 

throughout the entire range under study. Math~matically, the equation to a straight line 

is given as: 

Y = a + bX where, 

Y is a dependent variable; a is an intercept (a constant); 

b is a slop (regression coefficient) and 

X is an independent variable 

The graphical representation of a linear relationship is a straight line, that is the 

shortest distance between any two points, looks as:

(Graphical representation of a straight line) 

The value of dependent variable (Y) can be determined by using the above 

mathematical representation, corresponding to a given value of the independent 

variable X (within the range of X values). 

When there are more than one independent variable, say p independent 

variables (XI, Xz, ... X), the simple linear function form of mathematical representation 

i.e. equation is as follows: 

Where, a is the intercept (constant and the value of Y when all X's are zero) and 

bl 's are partial regression coefficients associated with the independent variables XI 

This represents the amount of change in Y for each unit changes in Xl,s. 

When the values of b, 's are not zero, it indicates the dependence of Y on Xis. 

Hence test of significance of b,'s are necessary to determine whether or not b = 0 is an 

essential for the regression analysis. Sometimes we may also seek to test the 

significance of the intercept to know whether or not a = a, where a, is any value 

specified by us. For example, if we wish to determine whether Y = 0 when Xlbs in the 

equation is zero. This is nothing but to check whether the line passes through the origin. 

For this, we must test whether or not a = 0. 

Homogeneity of Regression Coefficienl: 

When several linear regressions are estimated (due to different 

environments), it is usually important to determine whether various regression 

coefficients (slopes) of regression lines differ from each other. This is what is known as 

testing the homogeneity of regression coefficients. 

Of course, the concept homogeneity of regression coefficient is closely related to 

the interaction effecls among different factors in Analysis of Variance. Regression lines 

having equal slopes (non s$nificance of b's) are parallel to each other indicating that 

there is no interaction effect among the factors. It is to be noted that homogeneity of

egression does not imply equivalence of regression lines. For two or more regression 

lines to be coincide, the intercepts and slopes must be homogeneous. 

MULTIPLE LINEAR REGRESSION 

Regression analysis, involving more than one independent variable, is called 

mulliple regression analysis. When all independent variables are assumed to affect the 

dependent variable in a linear fashion and independently of each other, the procedure is 

called multiple linear regression analysis. 

The multiple regression analysis involves eslimalion and test of significance of 

p+l parameters (a, b,, b, ,... b,) by means of F=-test employing Analysis of Variance 

(ANOVA). The slructure of ANOVA for regression analysis is as follows: 

SOURCE D.F SS MSS F 

Due lo Regression p 1 (b,)(~(x,Y) 

-+ RSS RSS/p + A NB 

Dev.from Regression n-p-1 Z (Y' - RSS) -+ ESS ESSln-p-1-4 6 

Total n-1 Z Y' 

hl 

Coeffic~ent of determlnatlon R' IS computed as R'IRSS whlch measures the 

contr~but~on of the llnear funct~on of p Independent varlables to the dependent varlable Y 

and ~ts square root that IS R IS mult~ple correlation coefficlent The coefficlent of 

determlnatlon IS generally expressed In percentage whlch Infers the total varlatlon In the 

dependent varlable contributed by the Independent varlables 

The computed F value is compared to the tabular F value of variance ratio table 

of Fisher & Yales with (n-p-1) degrees of freedom. If the computed value of F is greater 

than the tabular F value, we say that the estimated linear multiple regression is 

signlflcant at a specified level of significance. Generally, 5% (P=0.05) and 1% (P=0.01) 

level of significance are specified for agricultural experiments. 

The significance of linear regression indicates that some portion of the variability 

in dependent variable Y is explained by the linear function of independent variables Xi 

Coefficient of determination, denoted as RZ (square of the multiple correlation) provides 

the lnforrnation on the size of that portion. Hence, if the value of R2 is high then the 

regression equation explains better. On the other hand, it the value of RZ is low, even if 

the F test is significant, the regression equation may not be of any meaning to the 

experimenter. It is also true that the value of ~'Increasee with the increase in number of 

independent variables. Care should be taken to discard the variables which are highly 

correlated among themselves. The analysis becomes cumbersome when independent 

variables increase considerably.

Two important points are to be kept in mind while going for linear regression 

analysis. 

P The effect of each and every independent variables on the dependent variable 

should be linear. That is the amount of change in Y per unit change in XI is constant 

through out the range of XI values under study. 

P The effect of each XI on Y should be independent of other X 

Violation of any one or both the above mentioned points leads to what is known as 

non-linear relationship. 

SIMPLE NONLINEAR REGRESSION 

Functional relat~onship between two variables is said to be nonl~near if the rate of 

change in dependent variable Y per unit change in independent var~able X is not 

constant. It is quite common to have such nonlinear relationship in biological organism. 

When the relationship among variables is not linear, the regression analysis is 

inadequate and therefore one must go for nonlinear regression analysis. 

A few mathematical models which are frequently encountered in applied 

research are given below: 

i) 'f=abx 

ii) Y = a + blX 

iii) Y = a + bX + cx2 

These nonlinear relationship can be made linear by simply transforming either 

one or more variables and then the procedure of linear regression technique can be 

applied. 

Equation (i) can be made linear by taking logarithm both sides. Similarly 

equation (ii) can be made linear by taking 1IX as X'. In case of (iii), an additional term 

cx2 is added in to the equation to a straight line. Here the additional term was created to 

make the model linear. 

MULTIPLE NONLINEAR REGRESSION 

When the relationship between the dependent variable Y and a set of 

independent variables Xi 's is not linear, it is said to be multiple nonlinear relationship. 

Following are the reasons for the existence of nonlinear relationship.

i) At least one of the independent variables exhibits a nonlinear relationship with 

the dependent variable. 

ii) 

If any two independent variables interact with each other. 

The analytical procedure for nonlinear relationship becomes cumbersome when 

the independent variables Increase. 

HOW TO FIND THE BEST FUNCTIONAL RELATIONSHIP 

in order to search for best functional relationship, several techniques are 

available. The most commonly used techniques for identifying best relationship among 

variables are (i) scatter d~agram technique, (ii) lest of significance technique and (iii) 

step wise regression technique. 

(i) Scatter diagram technique: 

It 1s most simplest and commonly used technique in determining the relationship 

between two var~ables. In this technique, all the pair of values of X and Y variables are 

plolted(as dots) in X-Y plane to get a scattered diagram. This diagram can be 

examined to ensure the pattern of the functional relationship. 

(~i) Test of significance lechn~que : 

This technique is used to eliminate unnecessary variables in the regression 

equat~on. Based on this technique, regression coefficients which are non-significant can 

be dropped while obtaining the functional relationship. 

(iii) Step-wise regression lechnique: 

This lechnique Is almost similar lo the test of significance technique where in all 

significant variables are included in regression. This objective can also be achieved by 

employing step-wise regression technique in adding variables, one at time. Here it is 

kept in mind that some variables may be dropped while determining the functional 

relationship even if they are perfectly associated. 

SOME MISUSES AND MISINTERPRETATIONS OF CORRELATION AND 

REGRESSION ANALYSIS 

Correlation and regression analysis is one of the most powerful tool in agriculture. It 

leads to incorrect interpretation of the result if the analysis is misused. One of the most 

commonly misuse associated with correlation and regression is to generalize the 

functional relationship beyond the data range, that is by extrapolating the result out

sMe !he range of the data in lndepandent variable. The generalization of regresslon 

beyond the original date is risky and should be attempted with proper knowledge in 

biological phenomenon. 

Another area of misuse the functional relationship is the application of 

generalized results for substitution. As far as practicable, the method of substitution be 

avoided. Only in some limited cases where there is wide range of variation exist, the 

substitution can be employed. 

Some times data from individual replications are employed to find the functional 

relationship. Care should be taken to employ the mean data over all replications for 

determining the functional relationship. If in the ANOVA, significant difference is 

detected among replications, then data from individual replications can be considered if 

determining the functional relationship. 

In simple correlation analysis, if r turns out to be significant implies the presence 

of causal reiationship between two variables. Even though correlation analysis 

quantifies the degree of association, it cannot provide the reason for such association. 

A non-significant r value cannot be taken to imply the absence of any functional 

relationship between two variables. Two variables may have nonlinear relationship 

even if r value is non-significant.

ON SOME STATISTICAL PROCEDURES FOR ANALYSIS OF 

DATA FROM FIELD EXPERIMENTS 

G. R. Maruthi Sankar 

Cenfrsl Research lnsl~fufe for Dryland Agricullure, ICAR, 

Santoshnagar, Hydembad- 500 059 

Correlation and regression techniques are used for assessing the relationships 

and predictions of variables. The d~fferent procedures of correlation like simple, partial, 

multiple. Rank, intra-class and correlation ratio are useful for different situations for 

assessing the relationships of variables. The simple and multiple regressions will be 

useful for making prediction of a dependent variable through a set of independent 

variables in different situations. The estimates of correlation and regression coefficients 

are tested using different stalistical tests of significance for valid inferences. The criteria 

for assessing model selection, comparison of models, sensitivity of regression are 

discussed. Some of the problems like multicollinearity and extreme observations In the 

data analysis are also discussed. 

MEASURES OF CORRELATION 

I. Slrnple Correlation : It measures relationship between two variables 'X' and 'Y'. 

It ranges from -1 tc +l. 

Z XY - (Z X) (I: Y) 1 n 

r = ---------------------------------- 

\/IT~' - IT~)' /n) (u'- (TX)' in) 

2. Partlal Correlation (first order) : It measures the partial relationship between 

two variables 'Y' end 'XI' keeping the effect of a third variable 'X2' as constant. 

It ranges between -1 and +l. 

3. Partial Correlation (second order) : II measures partial relationship between 

two variables 'Y' and 'XI' keeping the effects of two other variables 'X2' and 'X3' 

as constant. It ranges between -1 and +l.

4. Multiple Correlation : It measures the correlation of a dependent variable 'Y' 

with a set of 2 or more independent variables 'X' together. It ranges between 0 

and 1. 

5. Rank Correlation : It measures correlation between ranks of two variables 

instead of the actual observations of variables. It ranges between 0 to 1 

where d I is the difference in ranks. 

6. Correlation Ratio : Correlation ratio 'q' is the appropriate measure of curvilinear 

relationship when the relationship between two variables is not linear. If 

relation is linear then q = r, otherwise q > r. It ranges between -1 to +I. 

7. Intra-class correlation : Intra-class correlation means within class-correlation. 

Here both the variables measure same characteristics. It Is Ihs correlation wilhin 

a variable with respect to some common characteristic. For example, we may 

work out intra-class correlation between yields of plots. Suppose there are A,, 

A2, ..... A, families with kt, k2, .....k. members, and let x , (i = 1,2 ,... n ; j 

=1,2, ..... k ,) denote the measurement on the jth member in the i th family, then 

intra-class correlation can be given as 

If k ,= k (i.e., if all families have equal members), then 

r = (1 I (k-I)] [(k a: 1 a 2, - 11 

where 0 "enotes the variance of X and 02 denotes the variance of means of 

families. The intra-class correlation ranges between - Ill (k-1)] and 1.

MEASURES OF REGRESSION 

1. Slmple Regression : It measures the functional relationship between a dependent 

variable 'Y' and an independent variable 'X' with estimates of an intercept (a) 

and a slope (I)). The estimates of a and P can be negative, zero or positive. The 

linear regression is given as 

2. Multlple Regresalon : If the dependent variable 'Y' is a function of a set of 

independent variables 'X', then the estimates of regression coefficients of 

different variables (p) along with the intercept (a) are estimated using the matrix 

algebra. The multiple regression of 'Y' through different independent variables 

can be given as 

@ 

= (x'x)" X'y 

The regression coefficients can be negative, zero or positive and would 

measure the rates of change in 'Y' for an unit change in the Independent variables 

3. Polynomlsl Regrrsslon : If the dependent variable 'Y' is a function of linear and 

other higher order effects of an independent variable 'X', then the polynomial regression 

is fitted to quantify the effects of a variable and its significance at different orders for 

prediction of 'Y'. The nth order polynomial regression of 'Y' can be given as 

Y .: a + pl x1 + ~1 xI2 + kxl3 + b4 xI4 + ------------ 

= (X'X).' X'y 

P 

+ Pn Xln 

The polynomial regression coefficients can be negative, zero or positive and 

would measure the rates of change in 'Y' for an unit change in the independent 

variable. 

TESTS OF SIGNIFICANCE 

1. Testlng slgnlflcance In data of large samples: If X is distributed as Normal with 

mean p and variance U, then Z = (X-p )lo is distributed as Normal with mean 0 

and variance 1. If 121 > 1.96, then the sample mean is inferred to be significantly 

different from population mean at 5 % level of significance. If JZI > 2.58, then the 

sample mean is significantly different from population mean at 1 % level of 

significance.

2. Testing single proportion : If P is proportion of success and Q=1-P is proportion of 

failure. If mean = n P and variance = n P Q, then Z = [X - n P] I (n P Q) is 

distributed as Normal with mean 0 and variance 1. Same conclusions as above. 

3. Testing difference of proportions : Let pl = X1 I nl and p2 = X2 I n2. Mean (pl) 

= P1 & Mean (p2) = P2. Variance (pl) = PI Q1 I nl & V (p2) = P2 Q2 I n2 

Z is distributed as Normal with mean 0 and variance 1. Same conclusions as above 

4. Testing a mean in small samples : if X is distributed as Normal with mean p and 

variance o, then 2 = [ (x-p) I (a1 sqrt (n)) ] is distributed as Normal with mean 0 

and varianca 1. Same conciusions as above. 

5. Testing differences of two means : If 3 1 andT2 are means and o,' and 02' 

are variances based on two samples w~th nl and n2 observations, then 

- 

x1 -X2 

z = ----------------------- 

(oI2 I nl) + (a: I n2) 

is distributed as Normal with mean 0 and variance 1. Same conclusions as above. 

6. Testing differences of standard deviatlons : If $1 and s2 are standard deviations 

of two samples with nl and n2 observations from Normal distribution with 

variances o12 and oz2, then 

sl - s2 

is distributed as Normal with mean 0 and variance 1. Same conclusions as above 

7. Testing the dlfference between sample correlation ( r ) and population 

correlation ( p) by making 2-transformation can be given as 

A 

z- z 

- 

z = log J G l 

z= 1,.JiGzTl

8. Testing the difference between two conelatlon coefficlanb 'rl' and 'rZ' by 

making 2-transformation can be given as 

ZI = log .d [(I + rl) 

- rl)l 

9. Testing the observed correlation 'r' between two variables against 'zero' can be 

given as 

r 

10. Testing the partial correlation 'r' between two variables keeping the effects of 

a third variable as constant can be given as 

11. Testing the regression coefficlent (slope) 'p' of an independent variable 'X' can 

be given as 

P 

1 = ----------------------- 

12. Testltlg the regrossion coefficient (intercept) 'a' can be given as 

a - a' 

t = --* -------------------- 

13. Testing tile liomogeneity of regression coefficients (slopes) of 'k' sets of data 

(or over different seasons) can be given as

This is distributed as F with (k-1. Z n - 2k) degrees of freedom 

G=D-E~IC 

B = sum of 'residual sum of squares' of k sets 

C = sum of 'corrected sum of squares' of X of k sets 

D = sum of 'corrected sum of squares' of Y of k sets 

E = sum of 'corrected sum of squares' of products of X and Y of k sets 

14. Testing the predictability value (R') of a regression model with 'k' 

independent regressor variables can be given as 

SSR ~,EX1y+(+~X2Y+ +Pkzxky 

R2 = = ----------------------- 

*........................ *----- 

Z Y' 

Y' 

where SSR is sum of squares due to regression 

15. Testing the R' adequacy of a model with 'p' regressors compared to a model 

with 'q' regressors where p < q 

where F is wlth (q, n - q - 1) degrees of freedom 

where ~~a = 1 - ( l-~~~)(l+ d) 

16. Testing the sufficiency of a model with Residual Mean Square Ratio (RMSR) 

criterion : This is used for testing the sufficiency of a regression model with 'p' 

regressors compared to a model with 'q' regressors based on F-test and can be 

given as 

where RSS is residual sum of squares ; RMSS is residual mean sum of squares. 

This is distributed as F with (q - p . n - q - 1) degrees of freedom

17. Percentage Rnlative EMckncy (PRE) of a ngnsrlon model 'A' comparud to a 

regression model '0' can be given as 

aZB(n+q+1)/n 

PRE (A) - X 100 

02,(n+p+ 1)ln 

where and a' are Realdual mean sum of squares of models 'A' and '8' models 

PROBLEMS OF REGRESSION 

1. Multlcolllnearlty: High and significant correlations between different variables 

compared to the over all multiple correlation and predictability of a model. This 

will result in linear dependence of a variable on another variable and insensitive 

regression coefficients for prediction. The multicollinearity can be assessed by 

computing an estimate of X2 and can be given as 

x2 = - n - I - (116) (2k + 5) log , (D) 

where D = value of standardised determinant 

n = number of observations 

k = number of independent variables 

2. Examine the resldualr for Identifying 'Outilers, High leverage and Influential' 

observations, testing and deletion, and improving the predictability of models. 

The residuals can be examined in different forms as normalised , standardised, 

internally studentised and externally studentised residuals. 

Normalised : e I = f (el , eve) = el I \j e'e 

Standardised : b = f (el , o ) = el l a 

where o = \I- 

Internally studentised : r~ = f (e \ , a (1- p N) = e \/ o (I- p ,) 

Externally studentised : r , ' = f (e 1, o I (1 - p = e I I o 1(1 - p R) 

where at2 = o ,, (1 - p #)

, Effectm of variables on a model : Examine the effects of independent variables on 

the dependent variable for their sensitivity and significances, linear dependence 

and muiticollineaity, lack of homogeneity of variances, randomness of 

independent variables, autocorrelation of variables, extreme nature of variable in 

its relationships with other variables, violation of the assumption of normality and 

other aspects. 

4. Normal Distribution : The normal distribution can be given as 

(a) The curve is bell shaped and symmetrical about the line x = p 

(b) Mean, Median and Mode would coincide 

(c) As x increases numerically, f (x) decreases rapidly, the maximum probability 

occurring at the point x = p 

(d) pl = 0 (Skewness) and P, = 3 (Kurtosis) 

(e) Linear combination of independent normal variales is also a norrnal variate 

(f) Area property : P (p - o c X c (p + o) = 0.6826 

P (p -2 o c X c (p + 2 a) = 0.9544 

P (p -3 o c X < (p + 3 o) = 0.9973 

(g) x-axis is an asymptote to the normal curve 

(h) The points of inflexion of the curve are given by x = f (X) = 111 2 ] e +In1 

(i) Mean deviation about mean i s p CJ = (4 15) o 

(j) Quartile deviation = (Q3 - Ql) 12 = (2 13) 0 

(k)p2r+l=O(r=0,1.2.........) 

p2r "1.3.5 ........(2r-l)02r(r=0,1,2.........) 

This implies p 1 = 0 p 3 = 0 15 = 0 .......... (odd moments) 

pZ=I p4=3 ..........(even moments)

FUNDAMENTALS OF DESIGN AND ANALYSIS OF FIELD EXPERIMENTS 

WITH A NOTE ON TRANSFORMATION OF DATA 

Ravi R. Saxena and A. K. Roy* 

lndira Gendhl Agricultural Universiw 

Reipur-492012 M P. 

Bioinformalics Centre 

CIFA, Kausalyagangs, Bhubaneswar-751002, Orissa 

INTRODUCTION 

Experience has shown that proper consideration of statistical analysis before 

Ihe experiment is conducted, forces the experimenter to plan move carefully the design 

of experiment. The observations obtained from a carefully planned and welldesigned 

experiment In advance give entirely valid inferences. The subject-matter of the design 

of experiment includes. 

- planning of the experiment 

- oblaining the relevant information from it regarding the slatistical hypothesis 

under study 

- making statistical analysis of the data 

Somo Important definitions 

m n t 

: An experinienl is a device or a means of getting an answer to the problem 

under consideration e.g. comparison of different doses of feed or different 

species of fish etc. 

:The smallest division of the experimental material to which we apply 

the trealmenls and on which we make the observation on the variable under 

study e.g. in field experiments the plot of land is the experimental unit or pond 

may be experimental unit. 

&&n,ent: Various object of comparison in a experiment is called as treatments e.g. 

different species of fish or methods of cultivation are the treatments. 

w: Plot to plot variation under identical condition, which is due to 

random or chance factors beyond human control is known as experimental 

error. 

There are three important principles 

inherent in all experimental design. 

m: Replication means that a treatment is repeated two or more times, Its 

function is to provide an estimate of experimental error. 

m: Randomization is a process of assigning the treatments to various 

experimental unit in a purely chance manner. Its function is to assure unbiased 

estimates of treatment means and experimental error. 

of an F w :

Lacal : The process of reducing the experimental error by dividing the relatively 

heterogeneous experimental area into homogeneous experimental area into 

homogeneous groups is known as {ocal control. By reducing the experimenlal 

error, we can increase the efficiency of the design. 

Now, we shall discuss the layout and the analysis of the important designs of 

experiments 

Completely Randomlsed Designs (C.R.D.) 

- Simplest of all the designs, based on the principles of randomization and 

replication 

- treatments are assigned completely at random to each experimental unit. 

Hence, the CRD is only appropriate for experiments with homogeneous 

experirnental units, such as laboratory experiments, where environmental effects are 

relatively easy to control. 

Randomization and Layout 

For a experiment with four treatments TI, T2, T3 and T4 each repeated five 

times, the step-by-step procedures for randomization and layout of a CRD are as 

follows: 

Step 1. Determine the total number of experimental plot(n) by simply multiplying the 

number of treatment(t) and number of repetitions(r) i.e. n=(r)(t). For our 

example n=5x4=20. 

Step 2. Assign a plot number to each experimental plot is from 1 to n. For our 

example, the plot numbers 1.........20 are assigned to the 20 experimental plots 

as shown in following figure. 

Plot No. 

Treatment 

Step 3. Assign the treatments to the experimental plots by using any randomization 

schemes such as random number table or by drawing cards or by drawing lots, 

as given in figure.

Analyrla of variance 

- There are two sources of variation. One is the trealrnent variation, the other is 

experimental error. 

- A major advantage of the CRD is the simplicity in the computation of its analysis 

of variance, espedally when the number of replication is not uniform for all 

treatments. 

CUD for equal replication 

The steps involved in the analysis of variance of data from a CRD experiment 

with equal number of replication are given below. We use the data from an experiment 

in the laboratory using CRD with four pots and five varieties. 

Step 1. Arrange the data by treatments and calculate treatment total (T) and grand 

total (G) 

Step 2. Construct an outllne of the analyrir of varlrnce as followr 

Source of Degree of Sum of Mean Computed Tabular 

variation freedom sauares sauare F F 

Treatment 

Experlrnenlal 

error 

Total 

Step 3. Determine the degree of freedom (d.f.) for each source of variation, if t 

represent the number of treatments and r, the number of replications 

Total d.f. = (r)(t)-1 = (4)x(5)-1 = 19 

Treatment d.f. = t - 1 = 5 - 1 = 4 

error d.f. = t (r - 1 ) = 5(4-1) = 15 

error d.f, can be obtained through subtraction as 

Error d.f. = Total d.f. - treatment d.f. 

= 1Q -4 =I5 

Table 1. Experimental data obtained from an experiment 

Treatment Yield Treatment Treatment 

R 1 R2 R3 R4 Total Mean 

TI 25 21 21 18 85 21.2 

T2 25 28 24 25 102 25.5 

T3 24 24 16 21 85 21.2 

T4 20 17 16 19 72 18.0 

T5 14 15 13 11 53 13.2 

Grand total 397 

Grad mean 19.8

Step4: Calculate the correction factor and various sums of squares (SS) 

Correction factor (C.F.) = 

Treatment SS 

c TI 

- 1.1 

r 

C. F. 

= 331.30 

Error SS =.Total SS - Treatment SS 

Step 5: Calculate the mean square (MS) for each sources of variation by dividing 

each SS by its corresponding d.f. 

Treatment SS 

Treatment MS = 

I-1 

Error SS 

Error MS = - 

I(r- I)

Step 6: Calculate the F value for testing the significance of the treatment difference as 

I:= 

'Treatment MS 

Error MS 

Step 7: Obtain the tabular F-values 

fl = treatment d.f. = (t-1) = 4 

f2 = error d.f. = I(r-I) = 15 

For our example, the tabular F-values w~th fl=4 and f2 = 15 d.f. is 2.131 at 5% 

level of significance. 

Step 8: Enter all the computed values in the ANOVA table 

Source of Degree of Sum of Mean Computed Tabular F 

variation freedom squares squares F 5% 

Treatment 4 331.30 82.825 13.043. 2.131 

Experimental 15 95.25 6.35 

error 

Total 19 426.55 

' Significant at 5% level 

Step 9: Compare the computed F value with the Tabular F value and decide on the 

significance on the d~fference among treatments. For our example it is 

significant at 5% level of significance. 

Step 10: Compute the grand mean and the coefficient of variation (CV) as follows: 

Grand mean = Gln 

J~rror MS xiOO 

CC' = Grvld mean

For our example 

397 

Grand mean = - = 19.8 

20 

The CV indicates the degree of precision with which the treatments are 

compared and is a good index of the reliability of the treatment. It is generally placed 

below the analysis of variance table. 

CRD for unequal replication 

The CRD is commonly used for studies where the experimental material makes 

it difficult to use an equal number of replication for all treatments. Some examples of 

these cases are: 

- Feeding experiments where the number of fish for each breed is not the same 

- Experiments for comparing body weight and length of d~fferent species 

- Experiments that are originally set up wlth an equal number of replications but 

some experimental unit are likely to be lost or destroyed during experimentation. 

The analysis of variance for data from a CRD experiment with an unequal 

number of replications are given below. 

C.F. = ~ 'ln 

Total SS = 

1-1 

X: - C.F. 

' 1;= 

Treatment SS = x- - CF.. 

1-1 r, 

Error SS = Total SS - Treatment SS 

Follow the same procedure as given previously and complete the analysis of 

variance table. 

RANDOMISED COMPLETE BLOCK DESIGN (RCBD) 

Features : 

- Most widely used experimental designs in agricullural research. 

- Especially suited for experiments where the number of treatment is not large.

- Important feature of the RCB design Is the presence of blocks of equal size, 

each of which contains all the treatments. 

Randomization & layout : 

- Randomization process is applied separately and independently to each of the 

blocks. 

If there are six treatments TI, T2, T3 T4, T5 and T6 and three replications, we 

illustrate the procedure in the following steps. 

Step 1. Divide the experimental area into r equal blocks, where r is the number of 

replications. For our example, the experimental area is divide into three blocks. 

Ulock I. Block 2. Block 3. 

Step 2.Sub divide the block into t experimental plots where t is the number of 

treatments. 

Step 3. Assign t treatments at random to the t-plots applying any of the randomization 

schemes. For our example six treatments are assigned at random to the six 

plot using random number table. 

Step 4. Repeat the above steps for each of the remaining blocks 

Analysis of Variance 

- 

There are three sources of variability in RCB design; treatment, replication (or 

block) and experimental error. 

To illustrate the steps involved in the analysis of variance for data from a RCB 

design We use the data from an experiment that compared five varieties of fish given 

below in Table.

Step 1. Group the data hy treatments and replications and calculate treatment total(T). 

replication total (R) arid grand total (G) 

Step 2. Out line of the analysis of variance as foilom 


variation freedom squares square F F 

5% 1% 

Replication 

Treatment 

Error 

Total 

Table 2 yield of different varieties 

Variety Replication Total Mean 

I I I 111 IV 

V1 22.9 25.9 39.1 33.9 121.8 30.4 

V2 29.5 30.4 35.3 29.6 124.8 31.2 

V3 28.8 24.4 32.1 28.8 113.9 28.5 

V4 47.0 40.9 42.8 32.1 162.8 40.7 

V5 28.9 20.4 21.1 31.8 102.2 25.6 

Replication 157.1 142.0 170.4 156.0 

total(R) 

Grand total (G) 625.5 

Grand mean 31.30 

Step 3. Determine the degree of freedom for each sources of varlatlon. If r, represent 

number of replication and 1, the number of treatments, then 

Total d.f. = rt - 1 = 20 - 1 = 19 

Replication d.f. = r - 1 = 4 - 1 = 3 

Treatment d.f. = t - 1 = 5 - 1 = 4 

Error d.f. = (r - 1) (t - 1) = (3).(4) = 12 

The error d.f. can also be computed by subtraction 6s follows 

Error d,f. = Total d.f. - Replication d.f. - treatment d.f. 

= 19-3-4= 12 

Step 4. Compute the correction factor and various sums of squares (SS) as follows

i; R: 

Replication SS k!-- - C. F. 

1 

, 

1 7;' 

Trealment SS = LLr 

- C.F. 

Error SS = Total SS - Replication SS -Treatment SS 

= 351.10 

Step 5. Compute the mean square for each source of variation by dividing each sum of 

squares by its corresponding degree of freedom. 

Replication MS = 

Replication SS 

r-l 

Treatment SS 

Treatment MS = 

1-1 

Error MS = 

Error SS 

(r - l)(f - I)

Step 6. Compute the F value for testing the treatment difference as 

Treatment MS 

F, = 

Error MS 

Replication MS 

F, = 

Error MS 

Step 7. Compare the computed F1 value with the tabular F - values with f, = treatment 

d.f. and f2 = error d.f. and make conclusions. 

For our examole tabular F value with f, = 4 and f2 = 12 degrees of freedom is 

3.26 at the 5% level of significance. Because, the computed Fl value 4.448 is greater 

than the tabular F value at 5% level of significance, hence it is significant, we reject the 

null hypothesis. F2 is not significant at 5% level of significance 

Step 8. If result is significant compute critical difference and compare the treatment 

means for our example 

CD = t (at error d.f.) x is 

r 

From the bar chart it can be concluded that variety V, produces significantly 

higher yield than all other varieties. The remaining varieties are all on par 

Step 9. Compute the coefficient of variation as 

cv = GMS 

xlOO 

Grand Mean

Step 10. Enter all values compuled is above steps in the analysis of variance outline. 

The Final result of our example is shown below. 


variation freedom squares square F F 

Replication 3 80 80 26.93 < 1 

Variety 4 520.53 130 13 4.448' 

Error 15 351.10 29 25 

Total 19 952.43 

There are number of experimental deslgns viz. L.S D., split plot design, strip plot 

design etc, are avaitabie in the field of agricultural statistics, which can be used and 

analysed by using various available statistical packages. 

What to do when data break the rules 

Research workers who are content to learn the 'recipes" for carrying out an 

analysis of variance without attempting to learn and understand the underlying 

principles, may be headed for serious trouble. Whether they realize it or not, they are 

making certain assumptions about the data when they perform an analysis of variance. 

If the data do not conform to these assumptions, such an analysis may cause workers 

lo reach conclusion that are not justified. They may also overlook important conclusions 

that would be reached if the data were properly analysed. 

The assumptions underlying the analysis of variance are reasonably satisfied 

for most of the experimenlal data in agricultural research, but there are certain types of 

experiments that are notorious for frequent violations of these assumptions. 

Assumptions of Analysls of Variance (ANOVA) 

1. The error terms are randomly, independently and normally distributed. 

2. The variances of different samples are homogeneous. 

3. Variances and means of different samples are not correlated. 

4. The main effects are additive. 

The most common symptom of experimental data that violate one or more of 

the assumptions of the analysis of variance is variance heterogeneity.

Procedure for detecting the presence and type of variance heterogeneity 

- Compute the variance and the mean across replications for each treatment (the 

range can be used in place of the variance) 

- Plot a scatter diagram between the mean value and the variance 

- Examine, v~sually the scatter d~agrarn to identlfy the pattern of relalionship 

between mean and variance 

The following figure shows the three posstble outcomes of such an examinat~on 

Variance Variance Variance 

mean mean mean 

Fig.1 Fig.2 Fig.3 

Fia 1. Homoaeneous Variance 

~ i2. g ~eteri~eneous variance when the variance is functionally related to mean 

Fig 3. Heterogeneous variance when there is no functional relationship between the 

variance and the mean 

Transformation of data 

Data transformation is the most appropriate remedial measure for variance 

heterogeneity. In this techniques, the original data are converted into a new scale 

resulting in a new data set that is expected to satisfy the condition of homogeneity of 

variance. Because a common transformation scale is applied to all ~ bse~ati~n~, the 

comparative values between treatments are not altered and comparisons between 

them remain valid. 

The most commonly used transformations for data in agricultural research are: 

Logarithmic transformation 

Most appropriate for data where the standard deviation is proportional to the 

mean. 

Data that are whole numbers and cover a wide range of values e.g. number of 

insects per plot or the number of egg masses in per unit area etc. 

Take the logarithm of each and every component of data set.

lllurtration 

If the data set involves small values (e.g. less than lo), log ( x+ 1) should be 

used instead of log x, where x is the original data. 

An example for log transformation is given in the table below. 

Table 3. Observed and thelr log transformed values 

Original Valuer 

log valuer 

Treatment Replication Replication 

I I1 111 I I1 111 

Appropriate for data consisting of small whole numbers. 

For percentage data where the range is between 0 and 30% or between 70 and 

100% 

if most of the values in the data set are small (0.9. less than lo), especially with 

zeroes present, (xt0.5)'" should be used instead of xlR, where x is the original 

data, e.g. data obtained in counting rare events. 

lllurtration 

For illustration we use the following set of data on percentage of diseased tiller 

from a paddy variety trial of 6 varieties. The range of data is from 0 lo 21.99%. 

Because many of the values are less than 10, data are transformed in (x+0.5)"* as 

shown below. 

Table 4. Original and their square-root transformed valuer 

Original Values 

Transformed values 

Variety Replication Replication

Arc Sine Transformation 

- Appropriate for data on proportions, data obtained from a count, and data 

expressed as decimal fractions or percentage 

- It is not applicable to percantage data which are not derived from count data 

such as percentage of protein in rice, percentage of carbohydrates, infection 

index etc. 

- The value of 0% should be substituted by (114n) and the value of 100% by (100- 

114n) where n is the number of units upon which the percentage data was 

based. 

Certain rules for proper transforrnatlon scale for percentage data derived from 

count data 

Rule 1.For percentage data lying within the range of 30 to 70%, no transformation is 

needed. 

Rule 2.For percentage data lying within the range of either 0 to 30% or 70 to 100% but 

not both, the square-root transformation should be used. 

Rule 3. For percentage data that do not follow the ranges specified in either rule 1 or 

rule 2, the arc sine transformation should be used. 

Illustration 

We illustrate the application of arc sine transformation with data on percentage 

of fish survival trial with five size classes. For each variety 75 fishes were caught and 

the number of surviving fishes determined. 

Table 5. Percentage survival and their arc sine transformed valuer 

Survlval % 

Variety Original Values Arc Sine Scale 

Rl R2 R3 R1 R2 R3 

Based on rule 3, the arc Sine transformation should be used because the 

percentage data ranged from 0 to 100%. Before transformation all zero values are 

replaced by [1/4(75)] and all 100 values by [ 100 - {1/4(75))1.

ADVANCED STATISTICAL METHODS FOR DATA ANALYSIS 

R. N. Subudhl 

Berllampur Universfly, Bertrampur 

Onssa 

I. NON-PARAMETRIC TESTS 

While studying testing of hypothesis, we have used some tests (like large 

sample, t & F-tests) which estimated prameters of populations. Those are called 

parametric tests. In some cases we need not worry about the population parameters. 

Our test and result, both are about the sample observationlfunction (which is called a 

'statistic'), Such tests, as discussed below, are termed as non-parametric tests. 

Non-parametric tests are of course used for hypothesis testing. But it has also 

other extensive uses. And since it does not depend on the distr~bution of the parameter 

(of the population), there is no reference or comparison of tabulated value (like 1-table 

or F-table). It checks the pattern of occurrence of items in the sample. It assumes 

randomness or uniformity of the items. It checks whether the distribution of items (or 

whether the fit of the distribution is good or not, using chi-square). 

There are several non-parametric tests. Most of those tests check whether the 

items (or data) of the same series are appearing randomly ormnot. That is, whether 

successive items are changing (higher or lower than previous item) randomly or not. 

Here we discuss only two tests as given under, viz. SIGN TEST and RANK TEST. 

Sign test : In this test, average of the items of the given series is found first (say, M). 

Then each itemlvalue is deducted from this average or mean. Sign of such differences 

(XI - M) is noted. Suppose we get 'r' plus signs and 's' minus signs. If there are 'n' 

items in the series, then r + s s n. In this case our null hypothesis is that the chance of 

any item or value exceeding M is 112. That is, P {X>M)=P=112. Alternative hypothesis, 

HI : P > 112 (one tailed test) OR HI : P ;c 112 (two tailed test). 

In case there are zero - differences (when X = M) we have to ignore those 

cases. So, sample size will reduce to (rts). Statistic for comparison is : 

Wilcoxin has suggested further improvement to this simple sign test, as 

discussed below. The suggested test is popularly known as

Wilcoxin signed : Rank Test : Here, afler finding the differences of individual items 

from mean, we have to find ranks combinedly for all the differences, by taking absolute 

values of negative differences. Let T and T' be the sum of ranks of positive and 

negative differences respectively. 

SO that, f + T = 1 + 2 ..... t n = n (n+1)/2 

For test we can check any of the values T' or T or (T' - T) or (T* + T) 

While checking T', for large samples. N- (0,l) is assumed and the test statistic is 

given by the formula : 

(Here too, zero difference cases are ignored.) 

Run test (due to Wald - Wolfowitz) : A run IS a sequence of den tical letters (or sign) 

or by no letter at all Ex. + : ++ ~1 

In the above case there 4 runs In total, 2 of + and 2 of - signs 

false. 

If Ho is true then the number of runs (say r) wtll be large. If r is small, then Ho is 

We can convert any given series to a series of + - + etc by the following 

principle: 

If the succeeding item is higher than the previous term a + sign is written, if it is 

less, then a - sign is written. Tie cases are to be ignored. Let there are 'm' + signs 'n'- 

signs and furlher that r is even (= 2d, say). We should expect 'd' runs of + and another 

'd' runs of - signs In the series. For large sample cases, 

II. 

CLUSTER ANALYSIS 

Data collected by researcher has to be classified according to the need of the 

research design for analysis. Cluster analysis is a science of classification known 

previously as typology or taxonomy. Eventhough the science of classification

originated in ancient period, in modern times it was developed by a German 

anthropologist in 1914. But it was R. Tryon's book 'Cluster Analysis" in 1939, a 

psychologist, which established the analysis as an important tool for classification of 

entities. 

Cluster Analysis is a technique to group variables, individuals or entities. 

Geometrically it is defined as a 'continuous regibns of space containing a relatively high 

density of points separated by such other regions by regions containing a relatively low 

density of points. (B. Everilt, Cluster Analysis, 1980) 

The variables or entities can be grouped according to their similarity measures 

or according to their differences or distance measures. So far Karl Pearson's 

correlation coefficient is taken as a good similarity measure. lnspite of its importance it 

is not a good measure of similarity. Some of the important drawbacks are (a) It is 

sensitive to shape (b) Insensitive to the magnitude of variables (c) It is calculated on 

linear basis in which some entities remain unexplained. 

Similarltvcoan[clents : Similarity coefficients can be calculated for qualitative data or 

for quantitative data. For qualitative data similarity coefficients are calculated on a 

binary scale and presented In matrix form for clustering. There are so many formulas 

but Jaccad similarity coefficient is very popular among the cluster analysts which J = 

al(a + b + c) for quantitative data the formula is Ssk =1- (Xlk - Xjk)lRk, in which R is the 

range of the variables and Kk and Xlk entities. 

Dlatence : In this method the entities are clustered on the basis of their 

distances or differences which are called dissimilarity measure. One difference 

between the sim~larity and dissimilarity measure is that the former's value remains 

within 0 and 1 while the laler can take any positive value. One dimculty in distance 

measure is that it is scale dependent. But when raw data can be standardised lo 

calculate distance measures. For the calculation of distance measures Euclidean 

Metric measures formula Is used. 

A distance function can be transformed into a similarity function and vice versa. 

Technlnues : There are different types of clustering techniques of which 

hierarchical technique is most popular among the analysts since it is the simplest one. 

There are again two methods : agglomarative and divisive method. A dendrogram can 

be drawn to know the clusters either by single or complete linkage method. In divisive 

technique there are two methods (1) Monotonic and (2) Polithetic. The first method is 

the easiest. For this method the entities are divided into two subsets in any of the 2 n-

2 - I ways. The two groups are termed as main group and slinter group. Gradually 

one after another column is separated from the main group until it satisfies a certain 

condition. Then these two groups are further separated in the same procedure until no 

separation would be possible further. 

Ill. 

FORECASTING TECHNIQUES I AUTO CORRELATION 

By using past records or data, we can fit some mathematical models (or 

equations) through which we can estimate or forecast future values. There are several 

methods to do this, e.g. 

1) Fitting linear equations (or curves) by the method of least squares, (Normal 

equations computed from given data); 

2) Regression equationslmodels (including multiple regression models); 

3) Autocorrelation Analysis (In case of single time series data); and 

4) Time series models 

We here discuss the concepts of auto correlation briefly. It is very much useful 

for snalysing time-series data. 

Autocorrelation 

Autocorrelation is the correlation between time series componentslitems at 

different points of time. We can group each item with the successive ilem (or item at a 

fixed interval) to find the correlation. If time-lag is one (values at time t and t+l are 

paired), it is called first-ordered auto-correlation. 

'Prices of a company's equity share traded on daily basis", is an example of a 

time series. We can grouplpair the price of each day with that of next weeks price (time 

lag = 7 days). We can also make time lag as one day, as per the need. (In that case 

prices are paired with successive items). 

After the pairing (grouping of data), auto-correlation coefficient can be obtained 

by the formula similar to that of simple correlation coefficient (r). 

To know the significance of auto-correlation we can use 

Durbin-Watson d-statistic lies between 0 and 4. If d is very close (or equal) to 2, 

then it is un-correlated case. If dc2, there is positive autocorrelation (strongly positive if 

d=O) and if d72, it is negative auto correlation, (with strong negative auto~orrelation at 

d=4).

IV. 

MULTI-DIMENSIONAL SCALING (M.D.S.) 

4.1 Introduction 

In any problem of decision making (like buying a refrigerator or choosing a 

strategy) we find many alternatives. Several dimensions emerge when these 

alternatives are evaluated. Refrigerators can be described in terms of price, capacity, 

hours of trouble free operations, reputation of the manufacturer etc. Similarly in the 

case of employment decisions, choice involves salary, working conditions, opportunity 

for growlh and advancement, satisfaction etc. The search for an analytical approach to 

tackle such 'attribute-choice" problems has led to the techniques of multi-dimensional 

scaling. 

The development of various models and techniques of multi-dimesnional scailng 

is of recent origin, Initially it started with applications in Psychology. 

Subsequently these methods were used in marketing, Econom~cs, Operations 

research, Applied Statistics, Mathematical Psychology and Psychometrics. 

In multi-dimensional scaling, it is assumed that any object or brand (usually 

known as stimulus) can be described by levels on a set of attributes, characteristics or 

properties. The relevant attributes of the problem are determined by the decision 

maker. For example, in the purchase of a car, the attributes can be structural like 

strong body of a car, its colour, and speed. There may be functional attributes like the 

usability for long trips hauling. There may be psychological attributes like agreement of 

the characteristics of the car with the self concept. They may be social attibutes like 

people's perception of the type (of car) and of those who drive it. They may be 

economic attributes like initial cost, anticipated resale value and cost of maintenance. 

The stimulus may be presented to a respondent through : 

(i) 

(ii) 

(iii) 

(iv) 

(v) 

physical objects themselves 

pictorial representations 

verbalised profile descriptions 

name of objects 

any combination of the above 

Multi-dimensional scaling deals with Dsvcholoalcal among stimuli and 

expresses them through 

among points in a multi-dlrnensional 

sDace. 

The psychological relations are obtained through similarities and preferences.

Thus multidimensional scaling is the problem of representing n objects 

geometrically by n points. The interpoint distances correspond in some sense to 

experimental dissmilarities between objects. 

4.2. Multidimensional Scaling Models 

Multi-dimensional scaling models are classified into metric and non-metric models. 

In metric models, the input data may be assumed to be ratio scaled or interval 

scaled. In both the cases, the scaled distances found by the model are assumed to be 

metrically related. Given a set of interpoint distances these models find dimensionality 

and configuration of points whose distances most clearly match the input values with 

the smallest number of possible dimensions. 

ii) 

In many practical situations metric input data may not be available. People 

cannot ordinarily provide accurate and reliable data about equality relationships among 

objects such as competing brands or about brand characteristics. 

In non-metric models, only the ordinal or the rank order properties of the input 

data are considered. The objective of non-metric MDS methods is: ' Given rank order 

data, to find a configuration whose rank order of distances best reproduces, in a 

specified dimensionality the original rank order of the input data. 

4.3 Technique of Multi-dlrnesnional Scaling 

Multi-dimensional scaling is a technique of statistical fitting. The dissmilarities 

between flo pain of stimuli are given and we wish to find the configuration of n 

stimuli in a certain number of dimensions such that the distances between the stimuli fit 

the dissimilarities best. A criterion for the best fitting is given in terms of monotone 

relationship between the observed dissimilarities and the distances obtained from the 

fitted configuration. Symbolically if and SN: are two observed dissimilarities, and if dij 

and dil; denote the corresponding distances in the configuration then Sy < 6. implies that 

dl < d*. 

if we can find a configuration that is monotonically related to the observed 

dissimilarities, we say we have a perfect fit. However this may not be achieved 

especially in lower dimensions. We therefore need to have e criterion to evaluate the 

goodness of fit or badness. One standard criterion proposed by Kruskal is 'strees".

This 'stress' value can b computed for any configuration intended to represent the 

original set of dissimilarities. The lower the stress value, the better is the fit. 

The method of MDS is to start with some configuration in a given number of 

dimensions and iterate by finding new configurations with lower and lower stress value 

until a desired stress value is obtained. The final configuration is taken to be the best 

fit. Thus the procedure of MBS can be summarised in the following steps. 

i) For a given dimensionality, select some initial configuration X (This can be 

random configuration or provided by the experimenter) 

ii) 

iii) 

iv) 

Compute the distances dli between the stimuli pairs and evaluate it by 

computing the stress value S. 

If S> pre-specified cut off find a new configuration X whose ranks of dij are close 

to the ranks of the obse~edissimilarities. 

Repeat steps (ii) and (iii) until successive configurations converge 

v) Repeat (i) to (iv) in the next lower dimensionality and so on. 

vi) 

Choose the lowest dimensionality for which S is satisfactorily small 

4.4. Applications of MDS 

i) Market Segnientation 

A very promising area for application of non-metric scaling methods is market 

segmentation. A product class and its buyers could be represented as points in a 

space whose d~mensions are perceived product characteristics. Each brand could be 

represented as a stimulus point and each buyer as an ideal point. A market segment 

might be viewed as a sub-space of this superspace in which all members 

(a) 

(b) 

perceive the stimuli similarly 

possess the same ideal point position. 

Identification of such sub-space in which consumers exhibit commonality of 

perception and preference may reveal empty regions with a high concentration of ideal 

points and no close brands. Such an analysis would reveal the perception of different 

market segments about the competrtive position of the firm's brand and other brands. 

ii) 

Vendor Ev~luatlon 

An industrial purchasing agent may have to C ~ Q Oamong S ~ alternative vendors. 

One vendor may be low in price, fair on maintaining delivery promises, poor in technical 

service, and low in technical innovation. Another vendor may be high in prices but 

excellent in delivery promises. Each vendor can be represented as a point in multidimension 

space, the dimensions being the various criteria on which vendors are

selected. We are interested in the relative importance of each of the criteria, and how 

these weights vary over time. 

i) Advertising Evaluation 

MDS methods could be profitably used in an ad pre-testing in answering the 

following questions. 

(a) Are good ads more similar to each other than good ads are to bad ads 7 

(b) 

Do advertising personnel exhibit inter-person reliability in making similarity 

judgment 7 

(c) What are the dimensions along which ads are judged 7 

MDS method could also be extended to the problem of advertisement and 

vehicle matching. For example, what ads seam to go with what magazines 7 

iv) 

Brand Switching Research 

It might be of interest to couple studies of brand switching with those of 

similarities or preference analysis. Do brand switchers perceive products differently 

from brand loyal customers 7 What are the characteristics of preference structures for 

both brand switching and brand loyal types 7

AN OVERVIEW OF STATISTICAL PACKAGES 

Ravi R. Saxenr and A. K. Roy 

lndim Gandhi Agricuduml Unhrsrsity, Reipur -49201 2 

Bioinfomatkis Centre, CIFA, Bhubeneswar-751002 

Due to the attention given in the computational and algorithmic sciences during 

the part decade a lot of innovations has taken made in this field. Computations which 

was not possible manually has come to the reach of researches owing to the 

development of various statistical softwares available in the market .Almost all the 

popular software packages has one component exclusively dealing with basis statistical 

calculations like Excel of Msoffice. Basfcally computations are done on Spread sheets 

creating data file. One thing may be kept in mind that even with fundamental knowledge 

of statistics, one has to spent a lot of time to explore the packages for all practical 

purposes. This chapter will be dealing with some softwares available in the market for 

performing statistical analysis of data. However the list is not exhaustive, there may be 

many more packages besides these mentioned below. 

1. SPAR1 (Statistical Package for Agricultural Research) 

This package has been developed for the statistical anaiysis of experimental 

data In plant breeding and Genetics. The present package includes the following 

program modules : 

- input data file 

- Diallel analysis 

Multivariate analysis 

Multiple, linear regression analysis 

- Cluster analysis 

Line X Tester analysis 

Path analysis 

Discriminant analysis 

Stability analysis 

Partial Diallel analysis 

Triple test cross anaiysis 

Combining Ability 

- Generation Means analysis, Scaling test, Joint Scaling test. 

- Print Result File. 

- Rundos commands. 

System requirements: 

\BM Compatible PC-XT, AT and SX-386 with 640 KB RAM and with Math Co- 

Processor.

Software availability: 

Indian Agricultural Statistics Research Institute, New Delhi. 

2. SPSS ( Statistical package for Soclal Sclencer) 

The SPSS package includes the follow program modules (Base, professional 

Stat., Adv.Stat., Trends Categories and LISREL). It is a comprehensive integrated 

system for statistical data analysis. 

- 

Scatterplot, Histogram. Box plot. Error bar, Auto Correlation plots. Time series, 

Inter polation and regression line 

- Frequencies, plots, Descriptive, Cross-tabulation, Tables, Correlation's, Case 

listings 

T-test, ANOVA, MANOVA, Non-parametric test 

Multiple regression, Non-linear regression, Log-linear, regression, CHAlD 

- Cluster Analysis, Factor analysis, conjoint analysis Discriminant Analysis, 

Logistic regression. 

- Exponential smoothing, ARIMA, XI1 ARIMA, Auto regression, Seasonality, 

Spectral analysis. 

- COX regression, logistic Manova, loglinear, ~urvival>robit etc. 


Microsoft windows 3.1. windows 95, 386 based personal computer (486 or 

higher recommended); 8 MB RAM minimum (8 MB recommended; with I% MB of Hard 

disk storage space. 


Wipro - Software products Division' Binary Semantics limited 

4011 A, Lavelle Road A-6, C-Block Community Centre 

3rd Floor, Basappa Complex (or) Nasraina Vihar 

Bangalore - 56000 

New Delhi-110028 

3. SYSTAT: The SYSTAT provlder the following statistleal snalysls 

Basic statistics, t-tests, correlation, regression and crosstable 

ANOVAIMANOVA 

- Bootstrapping, canonical and set correlations 

- Classification and regression trees 

- Cluster analysis, conjoint analysis, correspondence anatysis 

- Design of experiments (7 methods) 

- Factor analysis and principle components 

- Logistic regression and probit 

- Loglinear model 

- Multidimensional scaling and perceptual mapping 

- Non parametric tests

- Partially ordered scale analysis 

- Path analysis 

- Repeated measures 

- Signal detection 

- Survival analysis (7 distributions) 

- Time series (ARIMA) 

1-tests 

Two stage least squares 

- 13 probability dens~lies and random number generators 


Microsoft windows 3.1, windows 95, 386 based personal computer (486 or 

higher recommended); 8 MB RAM minimum (8 MB recommended with I% ME of Hard 

disk storage space, SVGS monitor. 


BINARY SYMANTICS LIMITED 

A-6. C-Block Community Centre, 

Naraina Vihar, 

New Delhi -1 10028 

4. SPBD (Statistical Package for Block Designs) 

There are three main modules of this package 

- Catalogue of BIB designs 

- Generation of the design and randomized layout 

- Analysis of the dala generated from a BIB design 

System requirement: 

IBM compatible PC-XT, at and SX-386 with 640 KB RAM 

Soft ware availability: 

Indian Agricultural Statistics Research Institute Library Avenue, New Delhi - 

110012 

6. Deslgn - Ease 8 Design - Expert 

The features of the software are : 

- Scatter plots to visualize raw data 

- One variable, multi-level design 

- Optimal resolution fractional factorial 

- Replicate, delete, re-block designs 

- handles missing or botched data 

- Response transformations

- View ANOVA for precise information 

Drag-able 2-0 contours 

Slim contour plots 

Edit colors, text & more to procure snappy reports 

Desirability graphs - histograms or ramp 

- Augment any design 


Minimum 486, 6MB windows 3.11951NT 3.51 



A-6, C-Block Community Centre, 

Naraina Vihar. 

New Delhi -1 10028 

6. Slgma stat 

Sigma stat is the only advisory Statistical software unique Advisor Wizard which 

analysis data, recommends the test to run, and runs it. Sigma stat handles missing and 

unbalanced data, automatically checks that data fits the underlying assumptions of 

statistical model. It there is a violation, sigma stat automatically warns and calculates a 

more appropriate report of all test results complete with its own analysis its features are 

- 'Mess" data handling 

Graph editor Customization 

- Detailed reports with explanation of results 

- Statistics - Descriptive Statistics. 1-test and analysis of variance (ANOVA) 

- Graphing -Scatterplot 

System requirements (32 -bit): 

WIN 95 or Windows NT 4.0, 468 or higher, 33 MHz, ME and 11 to 16 

MB hard disk space 





New Delhi -1 10028

7. STATISTICA 

The software provides the following feature 

- non parametrics 

- distribution fitting multiple regression 

- general non-linear estimation 

- general ANCOVNMANCOVA 

- Stepwise discriminant analysis 

- log-linear analysis 

- Confirmatory /exploratory factor analysis 

Canonical correlation 

Survival analysis 

a large selection of time series modeling I forecasting techniques 

- structural equation modeling with Monte Carlo simulations and much more 


Window 3.1 /WIN 85 8 MB RAM, 10-12 MB hand disk space 


Stat Soft, 

70, Janpath 

New Delhi-110001 

8. LINDO & LINGO 

The software solve inventory, transportation, project, management, forecasting 

problems with operation research on PC. 

- Fast linear, integer 8 quadratic optimizer for variety of problem capacities 

- Data input, editing, optimization, display, logical data enquiry, file handling and 

sensitivity analysis 


Minimum 486, 2 MB, Windows 3.11951NT 3.5 





New Delhi -1 10028

lndostat provides the statistics you need in a program you can use most easily. 

You get comprehensive data editing data management and extensive statistical 

capabilities. lndostat softwares are available for various disci~linesuch as : 

- Applied statistics (Curve fitting, stepdownlstepwise regression, experimental 

designs) 

Clustering pack 

- Econometrics 8 Psychology pack 

- Advanced econometric models 

- Operation research pack 

Multivariate pack 

Time series pack 

ARlMA modeling 

Geology pack 

- Graphics pack 

- Advanced Econometic Models 

- Acceptance sampling 

Plant-Breeding 8 Genetics pack 

Entomology pack 

Animal Science pack 

- Poultry pack, etc. 


IBM compatible PC-AT13861486 IPentium machine with minimum memory 

requirement is 640 KB. 


lndostat Services 

18, Rohini Apartments 

7-1 -3UA Arneerpet 

Hyderabad -500 018 

Other softwares like sample power, peakfit, Table curve sigma plot, Delta graph, 

math cad etc. are also available. 

10. SAS (Statistical Analysir Systems Institute) USA 

The SAS System is an integrated system of software products. The SAS 

System enables you to perform 

- data entry, retrieval, and management 

- report writing and graphics 

- statistical and mathematical analysis 

- business forecasting and decision support

- operations research and project management 

- applications development 

The core of the SAS System is base SAS software. It consists of the SAS 

language, a programming language that you use to manage your data procedures that 

are software tools for data analysis and reporting a macro facility a windwoing 

environment called the SAS Display Manager System. 

There are other software packages which require more are less same system 

requirements there are 

MINITAB, MICROSTAT, MSTAT-C, SHAZAM, TSP, LlNDO 

SCARP is a statistical package dealing with analysis of sample survey data techniques 

developed by IASRI(ICAR), New Delhi. 

Most popular graphic packages are the following: 

HARVARD GRAPHICS, SIGMA PLOT, DELTA GRAPH

EXCEL FOR STATISTICAL DATA ANALYSIS 

P. K. Satapathy, A. K. Roy and R. Dash 

Computer Seclion 

Central lnstitule olFnrshwaler Aquacullunr 

Kausalpgenga. Bhubaneswar 751002 

INTRODUCTION 

Evolution of electronic spreadsheets is the most significant factor in starting up a 

trend towards business microcomputinglstatistical data analysis electronically by users 

even if who are having littlelno programming knowledge. Amongst various spreadsheet 

packages which appeared in Information Technology market (like LOTUS 1-2-3, 

VISICALC, SUPERCALC, QPRO. EXCEL) LOTUS 1-2-3 was most popular t~ll Microsofl 

Office came into picture, where the MS-EXCEL was available. 

CAPABILITIES OF EXCEL 

EXCEL has several capabilities which include opening of a workbook; entering 

and editing data; building formulas to calculate values; managing l~st of data; formatting 

data; creating a chart, saving a workbook; opening and saving files from other 

spreadsheets; linking documents from other spreadsheets, etc. Besides these it has 

super capability for data analysis. 

STATISTICAL ANALYSIS OF DATA 

Microsofl Excel provides a set of special analysis tools called Analysis ToolPak. 

These tools include statistical analyses which one can apply to many types of data as 

well as analyses which are Anova : Single Factor; Anova : Two Factor with Replication; 

Anova : Two Factor without Replication; Covariance; Correlation; Descriptive Statistics; 

Exponential Smoothing; F-test : Two Sample for Variances; Histogram; Moving Average; 

Random Number Generation; Rank and Percentile; Regression; t-Test; Paired Two- 

Sample for means; t-Test : Two Sample Assuming unequal means, elc. Before using an 

analysis tool, it is required to enter and organise that required to be analyzed into 

columns or rows on worksheet, which is called as input range. Text labels in the first cell 

of a row or column may be included to identify the variables latter. When an analysis tool 

is used to analyze data in an input range. Microsoft Excel creates an output table of the 

results. To use an analysis tool, choose Data Analysis from the Tools Menu. In the 

Analysis Tools, box, select the name of the tool required. Then specrfy the input and 

output ranges and any other options required. 

DESCRIPTIVE STATISTICS 

The Descriptive Statistics tool generates a report of univariate statistics for data 

in the input range. The output values generated by the Descriptive Statistics tool are : 

Standard deviation of sample (sample variance), kurtosis, and skewness. These outputs 

are demed using the same algorithms used by the built-in functions STDEV, VAR, 

KURT, and SKEW, rrrpedively.

Arithmetic mean : It Is also referred as average and calculated by simply adding 

the numbers and divkling by how many numbers there are. 

up all 

Medlan : The median is the value !hat exactly separates the upper half of the 

distribution from the lower half. 

Med = L+ (05~ - cumf 

NED 1 

Population mean : To avoid confusion Greek letter p, pronounced 'mew', is the symbol 

for the population mean. 

Standard devlatlon : It la most widely used measure of variability which uses the 

deviation of each score from the mean, but the calculation, instead of taking the 

abaolute value of each deviation, square6 each deviation to obtain values that are all 

potltive in tign. 

Deviation formula : 

Mean formula : 

z score : The z score is simply a way of telling how far a score is from the mean in 

standard deviation units. 

z score - sample : 

z score - populatlon :

The Confidenca Interval Approach for Estimating r : In this approach, instead of 

talking about possible values that p may take, given sample X, it is better off to set up a 

confidence interval in which the true mean probably lies. 

95% confidence interval for p urlng population a : Xi 1.96af 

99% confidence Interval for p urlng population a : X 1 25SOj 

COVARIANCE 

The Covariance tool returns the average of the product of deviations of data 

points from their respective means. Covariance is a measure of the relationship between 

two ranges of data. 

The use of Covariance tool is to determine whether two ranges of data move 

together; that is, whether large values of one set are associated with large values of the 

other (positive covariance), whether small values of one set are associated with large 

values of the other (negative covariance), or whether the values in the two sets are 

unrelated. 

ANOVA 

Analysis of variance, or anova, is a statistical procedure used to determine 

whether the means from two or more samples are drawn from populations with the 

same mean. This technique expands on the tests for two means, such as the t-test. 

Anova : single factor tool performs a simple analysis of variance, which test the 

hypothesis that means from several samples are equal. Anova : two-Factor with 

replication performs an extension of the single-factor anova that includes more than one 

sample for each group of data. Anova : two-Factor without replication performs twofactor 

anova that does not include more than one sampling per group. 

CORRELATION 

The Correlation tool measures the relationship between two data sets that are 

scaled to be independent of the unit of measure. The population correlation calculation 

returns the covariance of two data sets divided by the product of their standard 

deviations.

und 

One can use the Correlation tool to determine whether two data sets move 

togelher; that is, whether large values of one set are associated with large values of the 

other (positive correlation), whether small values of one set are associated with large 

values of the other (negative correlation), or whether the values in the two sets are 

unrelated (zero correlation - the correlation tends toward zero). Unlike covariance, 

correlation is independent of the units of measurement. 

REGRESSION 

The Regression tool performs linear regression analysis. Regression fits a line 

through a set of observations using the least square methods. 

USING CHARTS TO ANALYZE DATA 

The ease of plotting graphs comes as a handy tool in EXCEL for ovserving the 

trends, the impact of one or more variables on other etc. The graphs help in illustrat~ng 

the behaviour of the data. For example. the dependence of two variables on each other, 

i e., how one changes with a change in other; how different variables bahave over a 

period of time, etc. With the help of graphs, these bahaviours are brought out more 

clearly. 

Crealing a Trendline : The first step in creating a trendline is to select the data series in 

which the trendline is associated with. Then choose the Trendline command from the 

Insert Menu. On the Type tab, select the type of trendline needed. On the options tab, 

one can give the trendline a name and specify other option. The regression trendlines 

are linearllogarithmidpolynomiallpowerlexponential The options like displaying the R- 

squared value, setting the Y-intercept, moving average, formatting a trendline, etc. are 

available. The Linear option creates the trendline using linear equation y = mx + b. The 

logarithmic option creates the trendline using the logarithmic equation y = clnx + b. The 

polynomial equation y = b + ccx+qx2+ ...+ c6. The power option creates the trendline 

using power equation y=~~b. The exponential option creates the trendline using the 

exponential equation y=cebx. 

FORECASTING 

Exponential Smoothing tool predicts a value based on the forecast for the prior period, 

adjusted for the error in that prior forecast, which uses a smoothing constant, a, the

magnlude of which determines how strongly forscasts respond to errors in the prior 

forecast. 

Moving Average tool projects values in the forecast period, based on the average value 

of the variable over a specific number of preceding periods. Each forecast value is 

based on the following formula : 

where N is the number of prior periods to include in the moving average. A, is the actual 

values at time j, and F, is the forecasted values at time j. You can use this procedure to 

forecast sales, inventory, or other trends. A moving average provides trend information 

that a simple average of all historic data masks. 

The supplementation of projections with several other calculations are possible 

for example, the standard error measure the relative accuracy of projected values. 

Another method, the weighted moving average forecast, includes a large interval 

and allows to assign vanous nonnegative weights to observations over time. 

In the above equation, W,, W, ,..., WN are nonnegative weights that sum to 1. W, 

is the weight at interval I; A, is the actual value at lime j, and F, is the forecasted value at 

time 1. Here,SUMPRODUCT funclion can be used to calculate a weighted average. 

T-Test : Paired Two-Sample for Means : The paired two-sample for means 1-Test tool 

performs a paired two-sample student's 1-test. This form of the 1-test tests whether a 

sample's means are distinct. It does not assume that the variances of both populations 

from which the data sets are drawn are equal. A paired test is appropriate whenever 

there is a natural pairing of observations in the samples, such as when a sample group 

is tested twice, before and after an experiment. In this case as this is a paired test, the 

two input ranges of data must contain the same number of data points. 

This analysis tool is Pearson Correlation derived by using the formula

where cH h the degree of freedom. Another output values generated by this analysis tool 

is Pooled Variance, which is derived using formula 

where S' is pooled variance. 

T-Test : Two-Sample Assumlng Equal Variance : The two-sample assuming equal 

variances t-Test tool performs a two-sample student's 1-test. This form of the t-test 

assumes that the means of both data sets are equal and is referred to as a 

homoscedastic I-test. The t-Test is used to determine whether the two samples' means 

are equal. 

T-Test : Two-Sample Assuming Unequal Variances : The two-sample assuming 

unequal variance 1-Test tool performs a two-sample student's t-test. This form of the test 

assumes that the variances of both ranges of data are unequal and is referred to as a 

heteroscedastic t-teat. This t-test is used to determine whether two sample means are 

equal. This test is used when the groups under study are distinct. Use of a paired test is 

done when there is one group before and after a treatment. The formula used lo 

determine the test statistics value t is 

The formulae below is used to approximate the degrees of feeedom. The result 

of the calculation is usually not an integer. The nearest integer is used to obtain a critical 

value for the t table. 

df = 

(s; /my (s; 1.y + 

m-1 n-l 

F-Test : Two-Sample for Variances : The two-sample for variances F-Test tool 

performs a two-sample F-test. An F-test is a method for comparing two population 

variances. 

Z-Test : TwoSample for Means : The two-sample for means z-Test tool performs a 

two-sample z-test for means with known variance. This procedure Is commonly used to 

test hypotheses about the difference between two population means.

INSTRUCTIONS FOR OPERATING MINITAB STATISTICAL PACKAGE 

Snbashi Basu 

Indian Stetisticel Institute 

203 8. T. Road, Cek~tte - 700035 

lntroductlon to Mhitab 

Minitab is used interactively but may be used in batch mode also. We will 

concentrate on running Minitab in the interactive mode. 

Running Minitab 

Start Minitab by double clicking on the Minitab icon in Windows. Miniteb will 

respond by opening a window and showing (MTB >) prompt. 

Input and Output of Data 

Small amount of data may be read into variables C1, C2 etc d~rectly by the 

command 

MTB > READ Cl C2 C3 

Here each line of input consists of one set of values corresponding to variable 

C1, C2 and C3, e.g. 1 .O, 3.0. 0.0. Next line will consist of another set of values of Cl. 

C2 and C3, and so on. {END) command denotes end of data input. 

Alternatively you may give all the values of C1 together by the command 

MTB > SET C1 

DATA> 1.0 1.5 2.0 

DATA> 2.5 3.0 

DATA, END 

When data are read from a file use the commands 

MTB > WRITE 'inputfile' C1, C2, C3 

where inputfile is where your data are stored. Data read from two files may be 

combined in Minitab either side-by-side or one top of the other, e.g. 

MTB > READ 'inputl' Cl-ClO 

MTB > READ 'input2 Cll-CZO

MTB > M ITE 'widefile' C1-C20 

MTB * WRITE 'tallfile' Cl-C10 

To select subsets of data 

MTB > COPY C1 INTO C2 USE 1,3:5 

This will copy the contents of C1 using rows 1, 3, 4 and 5 Into C2. 

MTB > COPY C1 INTO C2 USE C5 = 64,30:50 

This will copy the contents of Cl into C2 only if C5 is 64 or 30 to 50. 

MTB > SORT C1 C2 will store the sorted version of C1 into C2. 

In Minitab the constants are stored into K1, K2 etc. To save the entire session of 

Minitab, use the command MTB > SAVE 'filename' 

This will put the entire worksheet into a file This will contain all columns, stored 

constants and column names. Information may be retrieved from it by using the 

command MTB > RETRIEVE 'filename' 

Many commands in Minitab have a number of subcommands. To indicate that a 

subcommand is in order a semi--colon(;) is put at the end of the command line. After all 

subcommands are specified a period(.) indicates the end, e.g. 

MTB > PLOT C1 C2; 

SUBC, 

TITLE 'CHERRY TREE DATA'; 

SUBC> YLABEL'DIAMETER'; 

SUBCz XLABEL 'VOLUME'. 

For plotting Minitab does not open a new window. 

Mathematical and Statistical Operations 

Some examples of arithmetic and algebraic expressions are 

MTB > LET C1= (C2 + C30) ' 10 - 60 

MTB > LET C1= C1- MEAN(C1) 

MTB > LET K1 = MEAN(Cl)ISTDEV(Cl)

MTB > LET C3 = C1+ C2-2 

Individual elements of a column may also be address e.g. 

MTB > LET C3 = C2(3) C1 

Usual mathematical functions like {ABSOLUTE), (SQRT), {LOGE), {LOGTEN), 

{SIN). {ROUND) etc, are also available. Commands for basic statistics include 

{DESCRIBE), {ZINTERVAL), (ZTEST), (TINTERVAL), (TTEST), {TWOSAMPLE) etc. 

Simple Linear Regression 

The basic command for regression C1 on (say) 3 predictors C2. C3 and C4 is 

MTB > REGRESS C1 3 C2 C3 C4 

The command (BRIEF K) controls the amount of the output. K can be any 

integer from 1 to 3, and the larger the value of K the more output. Default value of K 

is 2. 

The subcommands for regression include (NOCONSTANT), (MSE), 

{COEFFICIENTS), {HI), {RESIDUALS), (PREDICT). {VIF) etc. 

Minitab can also perform stepw~se regression, e.g 

MTB > STEPWISE C1 C2--C7; 

MTB > STEPS = 3. 

{STEPS) controls the number of steps shown per page. At the end of each 

group Minitab asks whether to show more of the steps or to end the output. 

Analysis of Variance 

The basic command for one--way analysis of variance is 

MTB > AOVONEWAY C1-C3 

Here each column contains Ihe observations for one cell. There must be more 

than two cells, otherwise the analysis is equivalent to (TWOSAMPLE) command with 

(POOLED)(standard deviation) subcommand. (AOVONEWAY) does not require an 

equal number of observation in each Cell. 

When all data are stored in one column and a second column gives the levels. 

then use the command 

MTB > ONEWAY C1 C2 C3 C4

C3 and C4 are optional. If C3 is specified the residuals are stored in it. If C4 are 

specified the fined values are stored In it. (TWOWAY) performs a two-way analysis of 

variance for balanced dala. 

The command (ANOVA model) does analysis of variance for multiway balanced 

designs. Factors may be crossed or nested, fixed or random. (ANOVA) calculates all 

exact F-tests, prints expected mean squares and estimates variance components. You 

may specify your own tests, store residuals and fitted values and print call and marginal 

means. You analyze up to 50 response variables on one (ANOVA) command. To enter 

data you need one column for each response variable and one column for each factor. 

This means there is one row of the worksheet for each obse~ation. This row contains 

the value of each response variable and level of each factor. 

Because models can be quite long and tedious to type, a vertical bar indicates 

crossed factors and a minus sign removes terms, e.g., 

(ANOVA Y = A I B ( C) is equivalent to the three-factor model with all the three 

two--way and the three--factor interaction terms. 

ANOVA Y A ) B ) C ) D - A'B'C -- A'B'C'D 

is equivalent to the model 

Y = A B C D A'B A'C A'D B'C B'D C'D A'B'D A'C'D 

B'C'D 

If a factor is nested you must indicate that when using the bar, 

e.g. 

ANOVA Y = A I B(A) 1 C 

is equivalent to 

Y =A B(A) C A'C B'C(A) 

Useful subcommands to be used with (ANOVA) are (RANDOM), (FITS), 

{RESIDUALS), (MEANS), {TEST) etc. The command (GLM) is used to do analysis of 

variance with balanced and unbalanced design, analysis of covariance and regression 

analysis. 

Multivariate Analysis 

The command (PCA) does principal components analysis. Components can be 

calculated from the correlation matrix (default option) and output consists of the 

eigenvalues, the proportion and cumulative proportion of the total variance explained by 

each principal component and the coefficient for each principal component. Useful 

subcommands are (COVARIANCE), (COEF), (SCORES) etc.

The command (DISCRIMINANT) does linear and quadratic discriminant analysis 

for dassifying observations into two or mom groups based on the specified predictors. 

Output indudes the classification matrix, the squared distance between group centers, 

the linear discriminant function, means, standard deviations end covariance matrices 

and a summary of how each observation was classified. Useful subcommands are 

(QUADRATIC), (FITS), (XVAL), (PREDICT) etc. 

Plots and Graphics 

The basic command for scatterplot of C1 versus C2 is 

MTB > PLOT C1 C2 

To add titles, footnotes and axis labels to th~s plot you may use the 

subcommands (TITLE), (FOOTNOTE). (XLABEL), (YLABEL). To change the plotting 

symbol you may use the subcommand (SYMBOL}. The command (MPLOT) puts 

several plots on the same axes and (LPLOT) plots data using letters for plotting symbol. 

(TSPLOT) does a time--series plot. (HISTOGRAM) and (DOTPLOn produces 

histograms, The commands (GPLOT), (GMPLOT), (GLPLOT) etc are useful to produce 

high resolution graphics. (GPLOT) may also be used to plot a function. You may also 

control the line styles and colors for your graphs. 

References 

MINITAB Reference Manual : Release 7

yy," 1c1~n62 - Central Institute of Brackishwater Aquaculture

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