Introductory Statistics with RR is an Open Source implementation of the S language. It works on multiple computing platforms and can be freely downloaded. R is now in widespread use for teaching at many levels as well as for practical data analysis and methodological development. This book provides an elementarylevel introduction to R, targeting both nonstatistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. A supplementary R package can be downloaded and contains the data sets. The statistical methodology includes statistical standard distributions, one and twosample tests with continuous data, regression analysis, one and twoway analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last six chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression, and nonlinear regression. In the second edition, the text and code have been updated to R version 2.6.2. The last two methodological chapters are new, as is a chapter on advanced data handling. The introductory chapter has been extended and reorganized as two chapters. Exercises have been revised and answers are now provided in an Appendix. 
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Review: Introductory Statistics with R (Statistics and Computing)
User Review  Yuta Tamberg  GoodreadsNot only a good starting point, but also pretty useful handbook Read full review
Review: Introductory Statistics with R (Statistics and Computing)
User Review  GoodreadsNot only a good starting point, but also pretty useful handbook Read full review
Contents
2 The R environment  30 
3 Probability and distributions  55 
4 Descriptive statistics and graphics  66 
5 Oneand twosample tests  95 
6 Regression and correlation  109 
7 Analysis of variance and the KruskalWallis test  126 
8 Tabular data  145 
9 Power and the computation of sample size  155 
13 Logistic regression  226 
14 Survival analysis  249 
15 Rates and Poisson regression  259 
16 Nonlinear curve fitting  275 
A Obtaining and installing  289 
B Data sets in the ISwR package1  293 
C Compendium  324 
D Answers to exercises  337 
10 Advanced data handling  163 
11 Multiple regression  185 
12 Linear models  195 
355  
357  