R is an Open Source implementation of the well-known S language. It works on multiple computing platforms and can be freely downloaded. R is thus ideally suited 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. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered 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 four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis. Peter Dalgaard is associate professor at the Biostatistical Department at the University of Copenhagen and has extensive experience in teaching within the PhD curriculum at the Faculty of Health Sciences. He was chairman of the Danish Society for Theoretical Statistics from 1996 to 2000. Peter Dalgaard has been a key member of the R Core Team since August 1997 and is well known among R users for his activity on
Table of contents:
Basics.- Probability and Distributions.- Descriptive Statistics and Graphics.- One- and two-sample tests.- Regression and Correlation.- ANOVA and Kruskal-Wallis.- Tabular Data.- Power and the Computation of Sample Size.- Multiple Regression.- Linear Models.- Logistic Regression.- Survival Analysis.
Table of contents:
Basics.- Probability and Distributions.- Descriptive Statistics and Graphics.- One- and two-sample tests.- Regression and Correlation.- ANOVA and Kruskal-Wallis.- Tabular Data.- Power and the Computation of Sample Size.- Multiple Regression.- Linear Models.- Logistic Regression.- Survival Analysis.
From the reviews: TECHNOMETRICS "...extensive, well organized, and well documented...The book is an elegant R companion, suitable for the statistically initiated who want to program their own analyses. For experienced statisticians and data analysts, the book provides a good overview of the basic statistical analysis capabilities of R and presumably prepares readers for later migration to S...The format of this compact book is attractive...The book makes excellent use of fonts and intersperses graphics near the codes that produced them. Output from each procedure is dissected line by line to link R code with the computed result...I can recommend [this book] to its target audience. The author provides an excellent overview of R. I found the wealth of clear examples educational and a practical way to preview both R and S."