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A Handbook of Statistical Analyses Using R 1st Edition

4.6 out of 5 stars 7 customer reviews
ISBN-13: 978-1584885399
ISBN-10: 1584885394
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Editorial Reviews

Review

Useful examples are presented to assist understanding. …Everitt and Hothorn have written an excellent tutorial on using R to analyze data using a wide range of standard statistical methods. They use numerous examples throughout the text, present 100 figures, and show 54 tables to augment discussion. All this is done in a book of only 275 pages in length. I highly recommend the text for anyone learning R, and who want to use it for the sophisticated analysis of data.
-Joseph M. Hilbe, Emeritus Professor, University of Hawaii and Adjunct Professor, Sociology and Statistics, Arizona State University, Journal of Statistical Software, Vol. 16, August 2006

…The book is clearly meant to help a true beginner get started with the R package. It begins appropriately with a chapter presenting a description of R and installation instructions, the help (simple help) and vignette (detailed help) commands, and other available documentation. This chapter also discusses basic data handling techniques and methods for summarizing data. The remainder of the book consists of 14 chapters, each of which describes a different type of analysis. … The chapters are generally well laid out and easy to understand. The book covers ANOVA/MANOVA, several forms of regression, an assortment of multivariate analyses, and various other forms of statistical analysis. … For the experienced analyst wanting to learn R, this book is a useful, compact introduction.
-Biometrics

… This book, using analyses of real sets of data, takes the reader through many of the standard forms of statistical methodology using R. … a very valuable reference. …The book is particularly good at highlighting the graphical capabilities of the language. …
-P. Marriott (University of Waterloo, Canada), Short Book Reviews
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Product Details

  • Paperback: 304 pages
  • Publisher: Chapman and Hall/CRC; 1 edition (February 17, 2006)
  • Language: English
  • ISBN-10: 1584885394
  • ISBN-13: 978-1584885399
  • Product Dimensions: 9.2 x 5.9 x 0.6 inches
  • Shipping Weight: 8.8 ounces
  • Average Customer Review: 4.6 out of 5 stars  See all reviews (7 customer reviews)
  • Amazon Best Sellers Rank: #1,484,551 in Books (See Top 100 in Books)

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Top Customer Reviews

By Michael R. Chernick on March 7, 2008
Format: Paperback
Brian Everett has previously written similar handbooks for SAS and SPlus. As R is becoming the language of choice in statistical computing in research particularly academoc research this book is a welcome addition. This book is actually a great booj on statistical methods and covers most of the important modern advances including ANOVA, linear regression, generalized linear models with emphasis on logistic regression, probability density estimation (nonparametric), recursive partitioning (i.e. classification and regression trees), survival analysis, bootstrap methods, longitudinal data analysis including mixed effect linear models and generalized estimating equations, meta analyses, principal component analysis, multidimensional scaling and cluster analysis, In each case the methods are clearly explained, are illustrated using real data for examples using R code that is listed for the student to replicate. results are presented through computer output and graphs. This is a very diverse set of methods covering many topics and expecially those commonly needed in clinical trials. the book also contains a very useful bibliography. unfortunately Bayesian techniques are sorely missing with the only reference to Bayes being Schwarz's Bayesian Information Criterion (BIC) that is used for model comparisons.

This book helps open up sensible techniques thst can be applied to a wide variety of problems that the applied researcher might need. The only major technique that is missing here are the Bayesian hierarchical models that have been used extensively in the medical device arm of the FDA (CDRH) are not covered in this fine text.
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When it comes to working with statistics, R is a great tool to have at your disposal. Sadly, there is a shortage of information that closes the gap between the simplistic examples used to learn data analysis with R and the more complicated techniques necessary to use R when working with more complex data sets.

_A Handbook of Statistical Analyses Using R_ sits nicely between the traditional introductory tomes for R (Introductory Statistics with R by Peter Dalgaard, or Statistics: An Introduction using R by Michael J. Crawley being two of the best) and the more advanced single topic texts which have a tendency to focus on one particular modeling technique.

As a workbook, the examples are short enough to be worked through in anywhere from 30 minutes to two hours. And while they often assume that the reader is familiar with certain aspects of statistical analysis, a quick refresher is provided for most topics before the exercises.

As a quick reference used to give examples of how to analyze different types of data, the book stands out for having a diverse set of worked examples that give a great jump start into working with R if you need a sample to get going.

If you work with R long enough, you'll find that you need a variety of reference sources to draw upon. _A Handbook of Statistical Analyses Using R_ is a solid addition to that reference library.
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Format: Paperback
This book is an accessible, higly readable introduction to the R Language and applications in statistics. I have compared other books in the same category and I can find none that approach this book in its clarity of presentation. I highly recommend this book for anyone who is approaching this subject for the first time.
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I was torn between getting this and the big blue R book that all my colleagues rave about. I still plan on getting it some day, but for my purpose this book really helped me out with my research. I especially needed the chapter on regression tree analysis, and for that I am truly appreciative of this book. It starts off by using examples built into the R program. From there I was able to follow along. With trial and error I was able to adapt the code to use with my data. I would not have passed my thesis defense without this book.
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