The comparison considered the accuracy of the results as well as the ease with which the interface could be used for bigger data sets - i.e. We used this data to do some simple analyses and compared the results with a standard statistical package. The subjects are entered in the order that the data became available, so the data is not ordered in any particular way. We were unable to get a measurement for Y on the second subject, or on X for the last subject, so these cells are blank. X and Y are the values of two measurements on each subject. The first subject received Treatment 1, and had Outcome 1. Since almost all real data sets have at least a few missing data points, and since the ability to deal with missing data correctly is one of the features that we take for granted in a statistical analysis package, we introduced two empty cells in the data:Įach row of the spreadsheet represents a subject. It was chosen to have two categorical and two continuous variables, so that we could test a variety of basic statistical techniques. To present the results, we will use a small example. We decided to do some testing to see how well Excel would serve as a Data Analysis application. As a result, if you suddenly find you need to do some statistical analysis, you may turn to it as the obvious choice. It is easily used to do a variety of calculations, includes a collection of statistical functions, and a Data Analysis ToolPak. Newly purchased computers often arrive with Excel already loaded. IntroductionĮxcel is probably the most commonly used spreadsheet for PCs. However when you are ready to do the statistical analysis, we recommend the use of a statistical package such as SAS, SPSS, Stata, Systat or Minitab. Output is poorly organized, sometimes inadequately labeled, and there is no record of how an analysis was accomplished.Įxcel is convenient for data entry, and for quickly manipulating rows and columns prior to statistical analysis.Many analyses can only be done on one column at a time, making it inconvenient to do the same analysis on many columns.
Data organization differs according to analysis, forcing you to reorganize your data in many ways if you want to do many different analyses.
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