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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