Introductory Statistics with R

  • Peter Dalgaard

Part of the Statistics and Computing book series (SCO)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Peter Dalgaard
    Pages 1-29
  3. Peter Dalgaard
    Pages 31-53
  4. Peter Dalgaard
    Pages 55-65
  5. Peter Dalgaard
    Pages 67-94
  6. Peter Dalgaard
    Pages 95-107
  7. Peter Dalgaard
    Pages 109-125
  8. Peter Dalgaard
    Pages 145-154
  9. Peter Dalgaard
    Pages 155-162
  10. Peter Dalgaard
    Pages 163-184
  11. Peter Dalgaard
    Pages 185-194
  12. Peter Dalgaard
    Pages 195-225
  13. Peter Dalgaard
    Pages 227-248
  14. Peter Dalgaard
    Pages 249-258
  15. Peter Dalgaard
    Pages 259-274
  16. Peter Dalgaard
    Pages 275-288
  17. Back Matter
    Pages 289-364

About this book

Introduction

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.

Keywords

ANOVA Analysis Analysis of variance Curve fitting Descriptive statistics Fitting Open Source Regression analysis Survival analysis data analysis linear regression statistics

Authors and affiliations

  • Peter Dalgaard
    • 1
  1. 1.Dept. BiostatisticsUniversity of CopenhagenKoebenhavn KDenmark

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-79054-1
  • Copyright Information Springer Science+Business Media, LLC 2008
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-79053-4
  • Online ISBN 978-0-387-79054-1
  • Series Print ISSN 1431-8784
  • About this book