R
- R is a free software for general mathematical computation, statistical analysis, and graphics. It is similar to the S language and environment which was developed at Bell Laboratories. We recommend using R with RStudio, which has a more user-friendly environment.
Website: The R Project
Availability at Cornell public computing facilities
Recommended reading:
- Discovering statistics using R , by Andy P. Field. Covers a wide breadth of statistical techniques from basic data exploration to regression, ANOVA, MANOVA, and multilevel models.
- Introductory Statistics with R , by Peter Dalgaard. This book serves as a great reference for various statistical analyses that can be done in R. All the code necessary to obtain the output is provided. From one-sample and two-sample t-test, to linear models, survival analysis, and generalized linear models, this book covers a wide array of useful techniques.
- Mixed effects models and extensions in ecology with R , by Alain F. Zuur. This book provides an expanded introduction to using regression and its extensions in analyzing ecological data. As with the earlier book, real data sets from postgraduate ecological studies or research projects are used throughout. All the R code used to generate the output and analysis is given in the text. This also serves as a great reference for generalized linear models and mixed models.
- R for SAS and SPSS Users, by Robert A. Muenchen. This book introduces R using SAS and SPSS terms. It demonstrates which of the add-on packages are most like SAS and SPSS and compares them to R's built-in functions. It steps through over 30 programs written in all three packages, comparing and contrasting the packages' differing approaches. The programs and practice datasets are available for download.
Tutorials:
Helpful web pages:
- Resources to help you learn and use R from UCLA Academic Technology Services. Many, many resources.
- R Reference Card
JMP | MATLAB | Minitab | SAS | SPSS | STATA |