A practitioner’s guide to resampling for data analysis, data mining, and modeling / P.I. Good (Ed.). - Boca Raton, FL : Chapman & Hall/CRC, 2011. - ISBN 978-1-439855-50-8

Egon van den Broek

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    With sufficient books on data mining and more than enough books on statistics on the shelf, I found myself drawn by the “practitioner’s guide‿ aspect of this book’s title. Compact as the book is, it promised something different from all the books on my shelf. Consequently, this review will focus on the book’s use in practice. The preface advertises http://statcourse.com/PGsoftware.htm, which should provide “code for most resampling methods‿ and “code for many of the routines.‿ However, at the start of 2012, only a few months after the book was released, this Web site is not online. However, the main Web site, http://statcourse.com/, also mentioned in the preface, is online and provides various courses. Regrettably, for all of these courses, a considerable fee has to be paid. Not a single line of code is available. Moreover, when a “practitioner’s guide‿ is introduced, sufficient material should be available to “learn by doing‿ and, as such, counter the deficiencies in the mathematical foundation. Some exercises are available; however, the answers are missing. In addition, the reader is directed to http://www.statcrunch.com/ for some datasets. This Web site is not related to the book; why not provide datasets dedicated to the book? All this being said, I think that Good is a gifted writer. He presents statistics in a way that makes it easy for laypeople to grasp the basic ideas behind it. Moreover, he provides sufficient examples and, where possible, presents them in a context that immediately illustrates the relevance of the technique at hand. Good presents the foundation of statistics, simplifies where possible, but does not lose himself in oversimplifying too much. Additionally, for those interested, he provides a good index and a wealth of references that provide pointers for readers who want to learn more on a specific issue. Unfortunately, the typesetting of the formulas is not consistent throughout the book. This will make it unnecessarily difficult for practitioners to grasp the ideas behind the math. Moreover, the typesetting and images are of low quality--not what one would expect from a hardcover. Overall, Good relies on his vast experience and presents yet another introductory book on statistics. It provides the gentle introduction, as is claimed, which can indeed be used in a variety of sciences. Its foundation is good, but it feels a little outdated; thus, its added value is questionable. For those who want to save their money, I suggest another book [1], by the same author, as an interesting alternative. That book shows a significant overlap (to say the least) with the book reviewed. There are also other alternatives by the same author [2,3]. With those books, the publisher provides both an instructor’s manual and datasets for the exercises. [1] Good, P. I. Resampling methods: a practical guide to data analysis. Birkhäuser Boston, New York, NY, 2006. [2] Good, P. I. Introduction to statistics through resampling methods and Microsoft Office Excel. John Wiley & Sons, Inc., Hoboken, NJ, 2005. [3] Good, P. I. Introduction to statistics through resampling methods and R/S-PLUS. John Wiley & Sons, Inc., Hoboken, NJ, 2005.
    Original languageUndefined
    Pages (from-to)CR139920
    JournalComputing reviews
    Publication statusPublished - 28 Feb 2012


    • resampling
    • EWI-21612
    • HMI-IE: Information Engineering
    • guide
    • IR-85502
    • Data Mining
    • Modeling
    • Review
    • METIS-296041
    • Data Analysis

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