You are here

A Handbook of Statistical Analyses Using R

Brian S. Everitt and Torsten Hothorn
Publisher: 
Chapman & Hall/CRC
Publication Date: 
2010
Number of Pages: 
355
Format: 
Paperback
Edition: 
2
Price: 
54.95
ISBN: 
9781420079333
Category: 
Manual
[Reviewed by
Robert W. Hayden
, on
04/26/2011
]

This handbook is not easy to classify. It is neither a statistics textbook nor a software manual. It uses a number of datasets from the earlier Handbook of Small Datasets (Hand et al.) and may have been partly inspired by that volume. In a private communication, one of the authors of the earlier work expressed a hope that it would be followed by example analyses of the data. Going even further back in time we have The Minitab Handbook which did for Minitab what this volume attempts to do for R. That is to say, both provide an extensive selection of real data analyzed with the software du jour. In addition to the difference in software, there is a difference in level, The statistical topics covered in the Minitab book comprise the most elementary 30% of the topics covered in the book at hand.

Viewed as a collection of worked examples, this book has much to recommend it. Each chapter (there are 18) addresses a specific technique. Chapters open with two to a few datasets printed with a minimal explanation of context, followed by an analysis of each dataset in turn. A typical analysis might involve a standard approach modified by a tweak or two that illustrate what R can do. Usually one peculiarity is uncovered and the analysis takes one step in further analyzing the peculiarity. Many of the analyses then end here abruptly, which left this reader hanging. There seemed to be no attempt to look for other peculiarities nor to wrap up the analysis with an overall summary of what was learned from the analysis. Still the examples provide a wide variety of partial analyses and the datasets cover a diversity of fields of study.

The book is less successful in explaining the statistical techniques or the use of R. Often there is little explanation of R beyond “here is the code and here is the output.” While an occasional command is explained in detail, readers not already well-versed in R will find themselves constantly looking up details elsewhere in order to see just what the code says. The statistical techniques get very short summaries. These are often quite good given their brevity, at times even elegant, but they seem at much too high a level of abstraction to be of much use in following the examples. All the Greek letters and summation signs do not help the reader figure out what that –1.467 in the R output means.

This handbook is unusually free of the sort of errors spell checkers do not find. There are some unsolved problems with page layout. Often there are too many pages separating the analysis from the data description and the R output. This is not an easy problem but it is one that has been addressed much more successfully, e.g., in the works on graphics by Edward Tufte.

Perhaps the best audience for this work would be people teaching some of this material to upper division or graduate students. For them it could provide a number of data examples and sketches of analyses. The students might be asked to complete the analyses. The teacher would already know the statistics and ideally a good deal of R as well.

References:

Hand, D. J., Daly, F., McConway, K., Lunn, D. and Ostrowski, E (1993), A Handbook of Small Datasets, Chapman and Hall.

Ryan, B. F, Joiner, B. L. and Cryer, J. D. (2005), The Minitab Handbook, Brooks/Cole (first published in 1976).


After a few years in industry, Robert W. Hayden (bob@statland.org) taught mathematics at colleges and universities for 32 years and statistics for 20 years. In 2005 he retired from full-time classroom work. He now teaches statistics online at statistics.com and does summer workshops for high school teachers of Advanced Placement Statistics. He contributed the chapter on evaluating introductory statistics textbooks to the MAA's Teaching Statistics.

An Introduction to R
What Is R?
Installing R
Help and Documentation
Data Objects in R
Data Import and Export
Basic Data Manipulation
Computing with Data
Organizing an Analysis
Data Analysis Using Graphical Displays
Introduction
Initial Data Analysis
Analysis Using R
Simple Inference
Introduction
Statistical Tests
Analysis Using R
Conditional Inference
Introduction
Conditional Test Procedures
Analysis Using R
Analysis of Variance
Introduction
Analysis of Variance
Analysis Using R
Simple and Multiple Linear Regression
Introduction
Simple Linear Regression
Multiple Linear Regression
Analysis Using R
Logistic Regression and Generalized Linear Models
Introduction
Logistic Regression and Generalized Linear Models
Analysis Using R
Density Estimation
Introduction
Density Estimation
Analysis Using R
Recursive Partitioning
Introduction
Recursive Partitioning
Analysis Using R
Scatterplot Smoothers and Generalized Additive Models
Introduction
Scatterplot Smoothers and Generalized Additive Models
Analysis Using R
Survival Analysis
Introduction
Survival Analysis
Analysis Using R
Analyzing Longitudinal Data I
Introduction
Analyzing Longitudinal Data
Linear Mixed Effects Models
Analysis Using R
Prediction of Random Effects
The Problem of Dropouts
Analyzing Longitudinal Data II
Introduction
Methods for Nonnormal Distributions
Analysis Using R: GEE
Analysis Using R: Random Effects
Simultaneous Inference and Multiple Comparisons
Introduction
Simultaneous Inference and Multiple Comparisons
Analysis Using R
Meta-Analysis
Introduction
Systematic Reviews and Meta-Analysis
Statistics of Meta-Analysis
Analysis Using R
Meta-Regression
Publication Bias
Principal Component Analysis
Introduction
Principal Component Analysis
Analysis Using R
Multidimensional Scaling
Introduction
Multidimensional Scaling
Analysis Using R
Cluster Analysis
Introduction
Cluster Analysis
Analysis Using R
Bibliography
Index
A Summary appears at the end of each chapter.

Dummy View - NOT TO BE DELETED