Torsten Hothorn, Brian S. Everitt

"... 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

• 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 Additive Models. Survival Analysis. Quantile Regression. Analyzing Longitudinal Data I. Analyzing Longitudinal Data II. Simultaneous Inference and Multiple Comparisons. Missing Values. Meta-Analysis. Bayesian Inference. Principal Component Analysis. Multidimensional Scaling. Cluster Analysis. Bibliography. Index.

Torsten Hothorn, Brian S. Everitt

"... 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

• 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 Additive Models. Survival Analysis. Quantile Regression. Analyzing Longitudinal Data I. Analyzing Longitudinal Data II. Simultaneous Inference and Multiple Comparisons. Missing Values. Meta-Analysis. Bayesian Inference. Principal Component Analysis. Multidimensional Scaling. Cluster Analysis. Bibliography. Index.