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Please note - Swinburne University of Technology

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eqsoo<br />

Research Methodology<br />

No. <strong>of</strong> hours per week: two hours<br />

Prerequisites: nil<br />

Instruction: lecturellaboratory<br />

Assessment: individual and syndicate<br />

assignments<br />

Subject aims and description<br />

This introductory research methodology subject aims to<br />

provide participants with a basic knowledge <strong>of</strong> the research<br />

methods that are necessary for successful market modelling<br />

in today's business world. As part <strong>of</strong> this subject, participants<br />

are expected to make extensive use <strong>of</strong> library resources and<br />

will be required to undertake a minor research project which<br />

is <strong>of</strong> direct relevance to the participant's areas <strong>of</strong> interest.<br />

Research methodology topics normally covered include:<br />

posing research questions and the measurement <strong>of</strong><br />

E! - concepts;<br />

5.<br />

I.- library research methods;<br />

0,<br />

m<br />

types <strong>of</strong> study<br />

-qualitative research methods<br />

6. - survey research methods<br />

3<br />

!? - experimental research methods;<br />

G<br />

--,- analysis and interpretation <strong>of</strong> results;<br />

A<br />

5 presenting a project outline and report writing<br />

-.<br />

(U<br />

3<br />

C,<br />

ID<br />

rn<br />

8<br />

techniques.<br />

Textbook<br />

Gay, L.R. and Diehl, P.L. Research Methods for Business and<br />

Management, New York, Macmillan, 1992<br />

E ~ ~ 5 0 7<br />

VI<br />

Q.<br />

ID<br />

n 3<br />

ID<br />

Market Modelling 1<br />

No. <strong>of</strong> hours per week: two hours<br />

Prerequisites: nil<br />

Instruction: lectureltutorial<br />

Assessment: individual assignments<br />

Subject aims<br />

This subject aims to introduce students to the concept <strong>of</strong><br />

developing models for product demand. Students will be<br />

introduced to a range <strong>of</strong> predictive techniques that may be<br />

incorporated into demand models. Evaluation <strong>of</strong> model<br />

outputs will be an important aspect <strong>of</strong> this subject.<br />

Note that throughout this course, extensive use will be made<br />

<strong>of</strong> computer s<strong>of</strong>tware packages.<br />

In this subject, a number <strong>of</strong> case studies based on a variety <strong>of</strong><br />

product markets will be analysed.<br />

Subject description<br />

Approaches to modelling and forecasting demand<br />

Forecasting model for stationary and non-stationary data<br />

Forecast monitoring and aggregation methods<br />

Autoregressive and moving average processes<br />

Model identification and evaluation<br />

Textbooks<br />

Makridakis, S., Wheelwright, S.C. and McGee, V.E. Forecasting<br />

Methods and Applications. 2nd edn, New York, John Wiley, 1983<br />

sqsog<br />

Business Modelling<br />

No. <strong>of</strong> hours per week: three hours for two<br />

semesters<br />

Prerequisites: nil<br />

Instruction: seminars and workshops<br />

Assessment: syndicate assignments<br />

Subject aims and description<br />

This subject is designed to give students an understanding <strong>of</strong><br />

the role <strong>of</strong> quantitative analysis in the decision-making<br />

process. The skills acquired are used in other subjects <strong>of</strong> the<br />

course as well as giving an appreciation <strong>of</strong> quantitative<br />

techniques via practical applications. User-friendly computer<br />

packages are employed throughout the subject wherever<br />

possible, reflecting their importance and usefulness.<br />

The topics included are: demography, linear programming,<br />

forecasting, inventory management, quality control and<br />

survey data analysis.<br />

Textbook<br />

Render, B. and Stair, R.M. Quantitative Analysis for Management. 5th<br />

edn. Boston, Allyn & Bacon, 1994<br />

BQ601<br />

Financial Modelling<br />

Subject aims and description<br />

The aim <strong>of</strong> this subject is to enable students to appreciate,<br />

and gain practice in the application <strong>of</strong>, a range <strong>of</strong> computer<br />

based analysis methods as components <strong>of</strong> a decision support<br />

system.<br />

Throughout the subject, extensive use will be made <strong>of</strong><br />

computer packages and particular emphasis will be given to<br />

current developments in computing that relate to finance and<br />

financial management.<br />

Topic coverage includes:<br />

Decision support systems, expert systems, microcomputers<br />

and current s<strong>of</strong>tware developments, financial modelling using<br />

spreadsheets, public data bases, approaches to risk analysis,<br />

evaluation and selection <strong>of</strong> computing systems.<br />

References<br />

S<strong>of</strong>tware documentation, user manuals and current journal articles<br />

will provide the major reference material for the subject.<br />

~ ~ 6 0 6 Business Demography<br />

No. <strong>of</strong> hours per week: two hours<br />

Prerequisites: nil<br />

Instruction: lecturellaboratory<br />

Assessment: individual and syndicate<br />

assignments<br />

Subject aims<br />

To introduce the basic methods <strong>of</strong> demographic analysis and<br />

to develop an awareness <strong>of</strong> the marketing implications <strong>of</strong><br />

demographic data. The demographic characteristics <strong>of</strong><br />

enterprises will also be examined with the aim <strong>of</strong> developing<br />

regional industry information banks. The subject aims to<br />

familiarise students to the full range <strong>of</strong> demographic data<br />

produced by the Australian Bureau <strong>of</strong> Statistics and the<br />

statistical bureaus <strong>of</strong> Australia's major trading partners.<br />

Subject description<br />

Sources <strong>of</strong> demographic data

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