## 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 elementary-level introduction to R, targeting both non-statistician 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 two-sample tests with continuous data, regression analysis, one- and two-way 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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### 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 | |