Stat-403/Stat-650 : Intermediate Sampling and Experimental Design ...

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The latter tool is but one of many contributions associated withErich Neuwirth (see sunsite.univie.ac.at/ Spreadsite/ ).Add-ins allow for a quick remediation of defective functionality,but may fragment the user community. For example, ifuser A uses add-in X but user B uses add-in Y while user Cuses the base spreadsheet processor, we may expect some—hopefully minor—differences in their statistical results. Unfortunately,even small differences give rise to worries that resultsmay be “wrong” and the causes of differences may be difficultto elucidate.Alternatively, like the Gnumeric community, one can attemptto provide a “best possible” spreadsheet processor. The marketdominance of Excel means in some cases including a wayto “work like Excel” even including its errors, but extra functionalityis also possible, such as Gnumeric’s hypergeometricfunction. Gnumeric.org also offers a set of test spreadsheets toallow a spreadsheet processor, and in particular new “builds”of Gnumeric, to be verified. See www.gnumeric.org for eitherthe open-source spreadsheet processor or the test sheets, whichare in .xls format. Unfortunately, these test spreadsheets are notnearly as extensive as one might like. The author invites interestedreaders to join him in helping to improve these.Gnumeric has already influenced statistical computing. JodyGoldberg, the lead maintainer of Gnumeric, found some improvementsto the statistical distribution function codes from Rwhich were used as the basis for Gnumeric’s functions. Theseimprovements have, I am informed, now been incorporated backinto R.com) provides an Excel add-in to do this, while Cluster Seven(www.clustersevern.com) uses (apparently) a large-scale enterprisegroupware system to monitor changes.5. WHEN SHOULD WE USE SPREADSHEETS INSTATISTICS?Data entry, edit, and transformation is first on my list of statsiticalapplications of spreadsheets. In the absence of a wellconfigureddatabase system, a spreadsheet with audit trail is easyto use and relatively “safe.” If functions are computed properly,we can perform transformations, recodings, and simple preliminaryanalyses. I find an audit trail serves best when it allows meto catch my own errors. Test calculations, as in the Gnumeric testspreadsheets, serve a similar role, but improvement is needed inthe usability, the capability, and the output of such tools.For statistical analysis, I use a spreadsheet for modest computationsthat can be programmed within the spreadsheet’s ownfunctions, avoiding special dialogs such as regression that (usually!)require user intervention to re-run them if data change orthat may vary in how they behave across spreadsheet processors(e.g., ANOVA). Graphics usually update if the inputs change, sothese are useful if they are simple enough to prepare, though myown preference is to use a statistical package. It is conceivablethat regression could be included within the regular spreadsheetfunctions by using vector-valued or selectable outputs. This is adirection I am investigating, as it would make these importantstatistical computations “updateable” with the data. A centraltheme, however, is that any analysis by spreadsheet should besimple to set up. A single formula applied to a large block ofdata is preferred to serveral formulas applied to only a few cells.4. AUDIT TRAILSTypical analyses where spreadsheets can be used:Audit trails help us find and correct errors. Most major statisticalpackages include this facility. Velleman (1993) presented • evaluation of probability distributions, as in solutions andsome ideas. For spreadsheets, we want to know who changed a marking guides for classroom exams, or computation of simpleparticular cell, when they changed it, and the content of the cell confidence intervals or hypothesis test results;before and after the change. Spreadsheets, traditionally, have notprovided this capability.There are several ways to include an audit trail with a spreadsheet.One is to note, as Neil Smith and I did in late 2002, that• data conversions, check totals, and modest tables;• simple trend lines or smoothings of data; and• simple descriptive statistics of columns or blocks of data.the change-recording facility of modern spreadsheet processorscould provide a log if we can ensure the change record is nottampered with or accidentally altered. The resulting changes listis large, requiring tools to filter it and ease the task of analyzingthe audit trail, for example, to show only those cells where aformula has been replaced with a number.After overcoming many annoying issues of technical detail,we found success by running the OpenOffice.org spreadsheetprocessor calc on a secure Web server. The details and other developmentshave been described in the references due to Adler,Nash, and Noël (2006). The server software is available underMacros should be avoided. These are programs that canbe launched (sometimes automatically and against the user’swishes) from within the spreadsheet. For Excel, macros are writtenin a form of Visual Basic. Other spreadsheet programs generallyallow similar constructs but using different coding methods.From the point of view of security and quality, macros raise alarge red flag since they often use random access to the spreadsheetdata in a way that is difficult to track and debug. Clearlyadd-ins can be criticized similarly if there is not a well-definedmechanism for interaction with the spreadsheet data.the Lesser GNU Public License at http:// telltable-s.sf.net. Descriptionsof the filtering program are available at http:// www. 6. SIDE BENEFITS AND SYNERGIEStelltable.com.We built TellTable using a Web interface in order to protectAnother approach, currently being tried in Gnumeric, is to our audited spreadsheet from interference. After the fact, weinclude the audit capability in the spreadsheet software itself. 13 discovered that it could run many software packages in a wayClearly it helps to have the source code (Nash and Goldberg2005).Finally, there are some commercial tools that claim to offeraudit trail capability. Wimmer Systems (www.wimmersystems.that allowed for controlled collaboration. Users normally “lock”a file to prevent conflicting edits, but a user can elect to share thescreen with one or more others who are at different locations.As a proof of concept we had two users share a single Matlab288 Statistical Computing Software Reviews
session where both could modify inputs to an animated graphicaloutput that was the solution of a set of equations. Collaborationon statistical modeling at a distance would be a similar task.Although many statstical packages provide a form of spreadsheetfor data entry and manipulation, these are often poor imitationsof general spreadsheet capabilities. Using standardizedfile formats, members of a team of users should be able eachto choose the tools they find most suited to their needs andtastes without fearing platform or file-format conversion difficulties.Dissociating information content from the tools usedto process it allows customization for individual productivitywhile maintaining group progress. For example, using ssconvertfrom Gnumeric allows me to move datasets and outputsback and forth easily between spreadsheets and R.The growth of Web-based applications and interfaces permitsmultiple, small, easily linked statistical applications to work onstandardized files. The building blocks exist now, are relativelystraightforward to program, and are usually platform independentas a bonus. Even on a local machine a Web interface is aconvenient way to build a graphical front-end to a set of simple,possibly non-windowed applications (Nash and Wang 2003).The three themes here—collaboration over distance and time,standardized files, and Web interfaces—are complementary toeach other and to spreadsheet use for selected statistical purposes.7. CONCLUSIONMy goal has been to highlight several technological developmentsin spreadsheets and computational practice that promiseimprovement in statistical data processing. A decade ago, Iwarned against spreadsheet use for any statistical application.Now I see the possibility of some useful, low-risk statisticalapplications of spreadsheets. Furthermore, statisticians, ratherthan complaining about the faults of spreadsheets, can becomeinsiders to open-source software projects that let them improvetheir own tools and the ways they use them.REFERENCESAdler, A., and Nash, J. C. (2004), “Knowing What was Done: Uses of aSpreadsheet Log File,” Spreadsheets in Education. Available online at http:// www.sie.bond.edu.au/ articles/ 1.2/ AdlerNash.pdf .Adler, A., Nash, J. C., Noël, S. (2006), “Evaluating and Implementing a CollaborativeOffice Document System,” Interacting with Computers, 18, 665–682.Brethour, P. (2003), “Human Error Costs TransAlta $24-million on ContractBids,” Globe and Mail (Toronto), online edition, Wednesday, Jun. 4, 2003,http:// www.bpm.ca/ TransAlta.htm.College Entrance Exam Board (2002), “Advanced Placement Program: StatisticsTeachers Guide.” Available online at apcentral.collegeboard.com/ repository/ap02 stat techneed fi 20406.pdf .Cryer, J. (2002), “Problems with using Microsoft Excel for Statistics,” in Proceedingsof the 2001 Joint Statistical Meetings [CD-ROM], Alexandria, VA:American Statistical Association.Gentleman, J. F. (2000), “Data’s Perilous Journey: Data Quality Problems and14 Other Impediments to Health Information Analysis,” Statistics and Health,Edmonton Statistics Conference 2000, Edmonton, Alberta, 2000.Hunt, N. (1995), “Teaching Statistical Concepts Using Spreadsheets,” in Proceedingsof the 1995 Conference of the Association of Statistics Lecturers inUniversities, Teaching Statistics Trust. Available online at http:// www.mis.coventry.ac.uk/ ∼ nhunt/ aslu.htm.McCullough, B. D. (1998), “Assessing the Reliability of Statistical Software:Part I,” The American Statistician, 52, 358–366.(1999), “Assessing the Reliability of Statistical Software: Part II,” TheAmerican Statistician, 53, 149–159.McCullough, B. D., and Wilson, B. (2002), “On the Accuracy of StatisticalProcedures in Microsoft Excel 2000 and Excel XP,” Computational Statisticsand Data Analysis, 40, 713–721.(2005), “On the Accuracy of Statistical Procedures in Microsoft Excel2003,” Computational Statistics and Data Analysis, 49, 1244–1252.Mount, A. M., Mwapasa, V., Elliott, S. R., Beeson, J. G., Tadesse, E., Lema, V.M., Molyneux, M. E., Meshnick, S. R., and Rogerson, S. J. (2004), “Impairmentof Humoral Immunity to Plasmodium falciparum malaria in Pregnancyby HIV Infection,” The Lancet, 363, June 5, pp. 1860–1867.Nash, J. C. (1991), “Software Reviews: Optimizing Add-Ins: The EducatedGuess,” PC Magazine, 10, 7, April 16, 1991, pp. 127–132.Nash, J. C., and Quon, T. (1996), “Issues in Teaching Statistical Thinking withSpreadsheets,” Journal of Statistics Education, 4, March. Available online athttp:// www.amstat.org/ publications/ jse/ v4n1/ nash.html.Nash, J. C., Quon, T., and Gianini, J. (1995), “Statistical Issues in SpreadsheetSoftware,” 1994 Proceedings of the Section on Statistical Education, Alecandria,VA: American Statistical Association, pp. 238–241.Nash, J.C., Smith, N., and Adler, A. (2003), “Audit and Change Analysisof Spreadsheets,” in Proceedings of the 2003 Conference of the EuropeanSpreadsheet Risks Interest Group, eds. David Chadwick and David Ward,Dublin, London: EuSpRIG, pp. 81–90.Nash, J. C., and Wang, S. (2003), “Approaches to Extending or CustomizingStatistical Software Using Web Technology,” Working Paper 03-24, Schoolof Management, University of Ottawa.Nash, J. C., and Goldberg, J. (2005), “Why, How and When Spreadsheet TestsShould be Used,” Proceedings of the EuSpRIG 2005 Conference on ManagingSpreadsheets in the Light of Sarbanes-Oxley, ed. David Ward, London:European Spreadsheet Risks Interest Group, pp. 155–160.Panko, R. R., and Ordway, N. (2005), “Sarbanes-Oxley: What About All theSpreadsheets?” Proceedings of the EuSpRIG 2005 Conference on ManagingSpreadsheets in the light of Sarbanes-Oxley, ed. David Ward, London:European Spreadsheet Risks Interest Group, pp. 15–60.Velleman, P. (1993), Statistical Computing: Editor’s Notes, The American Statistician,47, 46–47.14The American Statistician, August 2006, Vol. 60, No. 3 289
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