A Handbook of Statistical Analyses using R: 3rd Edition (Paperback) book cover

A Handbook of Statistical Analyses using R

3rd Edition

By Torsten Hothorn, Brian S. Everitt

Chapman and Hall/CRC

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Description

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, this handbook 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.

Reviews

“I truly appreciate how grounded in practicality this book is—and the way its chapters are structured really underlines this. Furthermore, all the datasets are interesting and vary widely in subject matter. If nothing else, this book is an excellent source of examples one might use to illustrate a variety of statistical techniques. … it offers a lot of good places to start if one wants to analyze data. … The book comes hand-in-hand with an R package, HSAUR3, with all the data and the code used in the text. The book is thus fully reproducible. Overall, it provides a great way for a statistician to get started doing a wide variety of things in the R environment. It would be particularly useful, then, for working statisticians looking to change their software. The book cites all the relevant packages one might need, which is quite nice for those attempting to navigate the vast array of packages freely available, and is quite clear in its presentation of the code. Between this and the datasets, it makes for quite a valuable and enjoyable reference.”

The American Statistician, August 2015

"… a handy primer for using R to perform standard statistical data analysis. … students, analysts, professors, and scientists: if you are looking to add R to your toolkit for analyzing data statistically, then this book will get you there."

—Kendall Giles on his blog, September 2014

Praise for 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

Table of Contents

Introduction

Density Estimation

Analysis Using R

Summary of Findings

Final Comments

Recursive Partitioning

Introduction

Recursive Partitioning

Analysis Using R

Summary of Findings

Final Comments    

Scatterplot Smoothers and Additive Models

Introduction    

Scatterplot Smoothers and Generalised Additive Models    

Analysis Using R    

Summary of Findings

Final Comments

Survival Analysis

Introduction    

Survival Analysis    

Analysis Using R    

Summary of Findings

Final Comments    

Quantile Regression

Introduction    

Quantile Regression    

Analysis Using R    

Summary of Findings    

Final Comments    

Analysing Longitudinal Data I

Introduction    

Analysing Longitudinal Data    

Linear Mixed Effects Models    

Analysis Using R    

Prediction of Random Effects    

The Problem of Dropouts    

Summary of Findings    

Final Comments

Analysing Longitudinal Data II

Introduction    

Methods for Non-Normal Distributions    

Analysis Using R: GEE    

Analysis Using R: Random Effects

Summary of Findings    

Final Comments    

Simultaneous Inference and Multiple Comparisons

Introduction    

Simultaneous Inference and Multiple Comparisons    

Analysis Using R    

Summary of Findings    

Final Comments

Missing Values

Introduction    

The Problems of Missing Data    

Dealing with Missing Values    

Imputing Missing Values    

Analyzing Multiply Imputed Data    

Analysis Using R    

Summary of Findings    

Final Comments

Meta-Analysis

Introduction    

Systematic Reviews and Meta-Analysis    

Statistics of Meta-Analysis    

Analysis Using R    

Meta-Regression    

Publication Bias    

Summary of Findings    

Final Comments    

Bayesian Inference

Introduction    

Bayesian Inference    

Analysis Using R    

Summary of Findings    

Final Comments    

Principal Component Analysis

Introduction    

Principal Component Analysis    

Analysis Using R    

Summary of Findings    

Final Comments

Multidimensional Scaling

Introduction    

Multidimensional Scaling    

Analysis Using R    

Summary of Findings    

Final Comments    

Cluster Analysis

Introduction    

Cluster Analysis    

Analysis Using R    

Summary of Findings    

Final Comments

Bibliography

Index

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General