Introductory Statistics With R

Peter Dalgaard (auth.)0-387-75958-6

R 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.

Peter Dalgaard is associate professor at the Department of Biostatistics at the University of Copenhagen and has extensive experience in teaching within the PhD curriculum at the Faculty of Health Sciences. He has been a member of the R Core Team since 1997.

Table of contents :
Front Matter….Pages i-xvi
Basics….Pages 1-29
The R environment….Pages 31-53
Probability and distributions….Pages 55-65
Descriptive statistics and graphics….Pages 67-94
One- and two-sample tests….Pages 95-107
Regression and correlation….Pages 109-125
Analysis of variance and the Kruskal–Wallis test….Pages 127-143
Tabular data….Pages 145-154
Power and the computation of sample size….Pages 155-162
Advanced data handling….Pages 163-184
Multiple regression….Pages 185-194
Linear models….Pages 195-225
Logistic regression….Pages 227-248
Survival analysis….Pages 249-258
Rates and Poisson regression….Pages 259-274
Nonlinear curve fitting….Pages 275-288
Back Matter….Pages 289-364