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A Handbook of Statistical Analyses Using R

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A Handbook of Statistical Analyses using R

Like the best-selling first two editions, A Handbook of Statistical...

NHBS Price: £39.99 $51/€48 approx
Shows how to obtain informative graphical output using R
Provides R code so readers can perform their own analyses
Emphasizes the practical application and interpretation of results rather than focusing on the theory behind the analyses
Offers an introduction to R, including a summary of its most important features
Contains many examples and exercises

By: Brian S Everitt (Author), Torsten Hothorn (Author)

275 pages, no illustrations

CRC Press

Paperback | Jun 2009 | Edition: 2 | #174963 | ISBN-13: 9781420079333
Out of Print Details

About this book

Doing for R what Everitt's other Handbooks have done for S-PLUS, STATA, SPSS, and SAS, A Handbook of Statistical Analyses Using R presents straightforward, self-contained descriptions of how to perform a variety of statistical analyses in the R environment. From simple inference to recursive partitioning and cluster analysis, eminent experts Everitt and Hothorn lead you methodically through the steps, commands, and interpretation of the results, addressing theory and statistical background only when useful or necessary. They begin with an introduction to R, discussing the syntax, general operators, and basic data manipulation while summarizing the most important features. Numerous figures highlight R's strong graphical capabilities and exercises at the end of each chapter reinforce the techniques and concepts presented. All data sets and code used in A Handbook of Statistical Analyses Using R are available as a downloadable package from CRAN, the R online archive.

A Handbook of Statistical Analyses Using R is the perfect guide for newcomers as well as seasoned users of R who want concrete, step-by-step guidance on how to use the software easily and effectively for nearly any statistical analysis.

"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

___________________________________________________________________________________________________________

Praise for 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


Contents

An Introduction to R
What Is R?
Installing R
Help and Documentation
Data Objects in R
Data Import and Export
Basic Data Manipulation
Computing with Data
Organizing an Analysis

Data Analysis Using Graphical Displays
Introduction
Initial Data Analysis
Analysis Using R

Simple Inference
Introduction
Statistical Tests
Analysis Using R

Conditional Inference
Introduction
Conditional Test Procedures
Analysis Using R

Analysis of Variance
Introduction
Analysis of Variance
Analysis Using R

Simple and Multiple Linear Regression
Introduction
Simple Linear Regression
Multiple Linear Regression
Analysis Using R

Logistic Regression and Generalized Linear Models
Introduction
Logistic Regression and Generalized Linear Models
Analysis Using R

Density Estimation
Introduction
Density Estimation
Analysis Using R

Recursive Partitioning
Introduction
Recursive Partitioning
Analysis Using R

Scatterplot Smoothers and Generalized Additive Models
Introduction
Scatterplot Smoothers and Generalized Additive Models
Analysis Using R

Survival Analysis
Introduction
Survival Analysis
Analysis Using R

Analyzing Longitudinal Data I
Introduction
Analyzing Longitudinal Data
Linear Mixed Effects Models
Analysis Using R
Prediction of Random Effects
The Problem of Dropouts

Analyzing Longitudinal Data II
Introduction
Methods for Nonnormal Distributions
Analysis Using R: GEE
Analysis Using R: Random Effects

Simultaneous Inference and Multiple Comparisons
Introduction
Simultaneous Inference and Multiple Comparisons
Analysis Using R

Meta-Analysis
Introduction
Systematic Reviews and Meta-Analysis
Statistics of Meta-Analysis
Analysis Using R
Meta-Regression
Publication Bias

Principal Component Analysis
Introduction
Principal Component Analysis
Analysis Using R

Multidimensional Scaling
Introduction
Multidimensional Scaling
Analysis Using R

Cluster Analysis
Introduction
Cluster Analysis
Analysis Using R

Bibliography
Index


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Biography

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.

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