Introductory Statistics with R

Springer Science & Business Media, Aug 15, 2008 - Mathematics - 364 pages

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.

What people are saying -Write a review

User Review - Flag as inappropriate

This is a very good book to learn R with hands on exercises. The data sets that are used in the book are available and it is possible to cross check your doings.

User Review - Flag as inappropriate

Sir,
I am a lecturer in a department of Statistics in Jahangirnagar University in Bangladesh. I need this book. Please give me this book at free.
Thanks.
Md. Rezaul Karim
Lecturer
Department of Statistics
Jahangirnagar University.
Savar, Dhaka

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