Introductory Statistics with R

✍ Scribed by Peter Dalgaard (auth.)

• Publisher: Springer New York
• Year: 2002
• Tongue: English
• Leaves: 273
• Series: Statistics and Computing
• Category: Library

✦ Synopsis

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

Basics....Pages 1-44
Probability and distributions....Pages 45-55
Descriptive statistics and graphics....Pages 57-80
One- and two-sample tests....Pages 81-93
Regression and correlation....Pages 95-110
Analysis of variance and the Kruskal-Wallis test....Pages 111-127
Tabular data....Pages 129-138
Power and the computation of sample size....Pages 139-147
Multiple regression....Pages 149-158
Linear models....Pages 159-189
Logistic regression....Pages 191-209
Survival analysis....Pages 211-220

✦ Subjects

Statistics and Computing/Statistics Programs; Bioinformatics; Computer Appl. in Life Sciences