Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis.
New to the Third Edition
- Three new chapters on quantile regression, missing values, and Bayesian inference
- Extra material in the logistic regression chapter that describes a regression model for ordered categorical response variables
- Additional exercises
- More detailed explanations of R code
- New section in each chapter summarizing the results of the analyses
- Updated version of the HSAUR package (HSAUR3), which includes some slides that can be used in introductory statistics courses
Whether you're a data analyst, scientist, or student, A Handbook of Statistical Analyses using R shows you how to easily use R to effectively evaluate your data. With numerous real-world examples, it emphasizes the practical application and interpretation of results.
- An Introduction to R
- Data Analysis Using Graphical Displays
- Simple Inference
- Conditional Inference
- Analysis of Variance
- Simple and Multiple Linear Regression
- Logistic Regression and Generalized Linear Models
- Density Estimation
- Recursive Partitioning
- Scatterplot Smoothers and Generalized Additive Models
- Survival Analysis
- Analyzing Longitudinal Data I
- Analyzing Longitudinal Data II
- Simultaneous Inference and Multiple Comparisons
- Meta-Analysis
- Principal Component Analysis
- Multidimensional Scaling
- Cluster Analysis
- Bibliography
- Index
Brian S. Everitt is Professor Emeritus at King's College, University of London. Torsten Hothorn is Professor of Biostatistics in the Institut fur Statistik at Ludwig-Maximilians-Universität München.
Reviews of the second edition:
"I find the book by Everitt and Hothorn quite pleasant and bound to fit its purpose. The layout and presentation [are] nice. It should appeal to all readers as it contains a wealth of information about the use of R for statistical analysis. Included seasoned R users: When reading the first chapters, I found myself scribbling small lightbulbs in the margin to point out features of R I was not aware of. In addition, the book is quite handy for a crash introduction to statistics for (well-enough motivated) nonstatisticians."
– International Statistical Review (2011), 79
" [...] an extensive selection of real data analyzed with [R] [...] Viewed as a collection of worked examples, this book has much to recommend it. Each chapter addresses a specific technique. [...] the examples provide a wide variety of partial analyses and the datasets cover a diversity of fields of study. [...] This handbook is unusually free of the sort of errors spell checkers do not find."
– MAA Reviews, April 2011
"I find the book by Everitt and Hothorn quite pleasant and bound to fit its purpose. The layout and presentation [are] nice. It should appeal to all readers as it contains a wealth of information about the use of R for statistical analysis. Included seasoned R users: When reading the first chapters, I found myself scribbling small lightbulbs in the margin to point out features of R I was not aware of. In addition, the book is quite handy for a crash introduction to statistics for (well-enough motivated) nonstatisticians."
– International Statistical Review (2011),
"an extensive selection of real data analyzed with [R] [...] Viewed as a collection of worked examples, this book has much to recommend it. Each chapter addresses a specific technique. [...] the examples provide a wide variety of partial analyses and the datasets cover a diversity of fields of study. [...] This handbook is unusually free of the sort of errors spell checkers do not find. [...]"
– MAA Reviews, April 2011
Reviews of the first edition:
"Brian Everitt has joined forces with a recognized expert who displays an impressive command of this powerful environment [...] Much is to be learned in the small details that make this text interesting even for experienced users. [...] Special attention is given to graphical methods [...]"
– Journal of Applied Statistics, May 2007
"Useful examples are presented to assist understanding. [...] Everitt and Hothorn have written an excellent tutorial on using R to analyze data using a wide range of standard statistical methods. [...] I highly recommend the text for anyone learning R and who want to use it for the sophisticated analysis of data."
– Joseph M. Hilbe, Journal of Statistical Software, Vol. 16, August 2006
"a useful, compact introduction."
– Biometrics, December 2006
"[...] This book, using analyses of real sets of data, takes the reader through many of the standard forms of statistical methodology using R. [...] a very valuable reference. !The book is particularly good at highlighting the graphical capabilities of the language. [...]
– P. Marriott, ISI Short Book Reviews