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(07 Nov 2004,
FrankHarrell
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---+ 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: <nop>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 =[[http://socserv.socsci.mcmaster.ca/jfox/Misc/Rcmdr 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 -- Main.FrankHarrell - 26 Jun 2004
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Topic revision: r2 - 07 Nov 2004,
FrankHarrell
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