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3rd Edition

A Handbook of Statistical Analyses using R





ISBN 9781482204582
Published June 25, 2014 by Chapman and Hall/CRC
153 B/W Illustrations

 
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Book 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.

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

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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