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.

Missing values are handled inconsistently, and sometimes incorrectly.

The problems we encountered that led to this conclusion are in four general areas: We concluded that Excel is a poor choice for statistical analysis beyond textbook examples, the simplest descriptive statistics, or for more than a very few columns. We used Excel to do some basic data analysis tasks to see whether it is a reasonable alternative to using a statistical package for the same tasks.

University of Massachusetts School of Public Health

Statistical tools for data analysis