Research Methods and Statistics Arena

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• Price: $59.95 $53.96
• Paperback: 376 pages
• Also available in e-Book
• Published: July 2009
• ISBN: 978-1-4200793-3-3
• Publisher: Chapman and Hall/CRC

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A Proven Guide for Easily Using R to Effectively Analyze Data

Like its bestselling predecessor, A Handbook of Statistical Analyses Using R, Second Edition provides a guide to data analysis using the R system for statistical computing. Each chapter includes a brief account of the relevant statistical background, along with appropriate references.

New to the Second Edition

• New chapters on graphical displays, generalized additive models, and simultaneous inference
• A new section on generalized linear mixed models that completes the discussion on the analysis of longitudinal data where the response variable does not have a normal distribution
• New examples and additional exercises in several chapters
• A new version of the HSAUR package (HSAUR2), which is available from CRAN

This edition continues to offer straightforward descriptions of how to conduct a range of statistical analyses using R, from simple inference to recursive partitioning to cluster analysis. Focusing on how to use R and interpret the results, it provides students and researchers in many disciplines with a self-contained means of using R to analyze their data.

Table of 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

A Summary appears at the end of each chapter.

Reviews

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

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

Author/Editor Biography

Brian S. Everitt is Professor Emeritus at King’s College, University of London.

Torsten Hothorn is Professor of Biostatistics in the Institut für Statistik at Ludwig-Maximilians-Universität München.