Statistical Software Recommendations

• Unfortunately learning time is directly proportional to power and flexibility of software
• Full power and flexibility is only possible when you go past menus to learn the package's command language
• Learning the command language allows one to practice reproducible research by being able to re-run entire analysis
• Excel is not a statistical package, and it contains severe errors. It also is tedious to use, especially when doing repetitive calculations or graphs
• In order of increasing power and learning time: SigmaStat -> SPSS -> SAS -> Stata -> S-Plus -> R
• R and S-Plus are superior for exploratory data analysis, graphics, and statistical modeling
• Stata is superior for statistical modeling and is good for graphics
• R is free to all from http://www.r-project.org; it uses virtually same commands as S-Plus but has limited menus. R runs faster, has slightly fewer bugs, has more add-on packages available, and is easy to install.
• An R add-on package R Commander (Rcmdr) adds SPSS-like menus to R
• There are many excellent online and published texts for learning R, and the Department of Biostatistics holds workshops
• See StatCompCourse for more information and handouts