421 pages, 153 b/w illustrations, 78 tables
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, A Handbook of Statistical Analyses using R 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.
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
- An Introduction to R
- Data Analysis Using Graphical Displays
- Simple Inference
- Conditional Inference
- Analysis of Variance
- Simple and Multiple Linear Regression
- Logistic Regression and Generalized Linear Models
- Density Estimation
- Recursive Partitioning
- Scatterplot Smoothers and Generalized Additive Models
- Survival Analysis
- Analyzing Longitudinal Data I
- Analyzing Longitudinal Data II
- Simultaneous Inference and Multiple Comparisons
- Principal Component Analysis
- Multidimensional Scaling
- Cluster Analysis
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