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© 2001

Resampling Methods

A Practical Guide to Data Analysis

Book

Table of contents

  1. Front Matter
    Pages i-xii
  2. Phillip I. Good
    Pages 1-22
  3. Phillip I. Good
    Pages 23-39
  4. Phillip I. Good
    Pages 40-59
  5. Phillip I. Good
    Pages 60-71
  6. Phillip I. Good
    Pages 72-86
  7. Phillip I. Good
    Pages 87-103
  8. Phillip I. Good
    Pages 104-121
  9. Phillip I. Good
    Pages 122-148
  10. Phillip I. Good
    Pages 149-159
  11. Phillip I. Good
    Pages 160-194
  12. Phillip I. Good
    Pages 195-201
  13. Back Matter
    Pages 202-238

About this book

Introduction

"Most introductory statistics books ignore or give little attention to resampling methods, and thus another generation learns the less than optimal methods of statistical analysis. Good attempts to remedy this situation by writing an introductory text that focuses on resampling methods, and he does it well."

— Ron C. Fryxell, Albion College

"...The wealth of the bibliography covers a wide range of disciplines."

---Dr. Dimitris Karlis, Athens University of Economics

This thoroughly revised second edition is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. It is an essential resource for industrial statisticians, statistical consultants, and research professionals in science, engineering, and technology.

Only requiring minimal mathematics beyond algebra, it provides a table-free introduction to data analysis utilizing numerous exercises, practical data sets, and freely available statistical shareware.

Topics and Features:

* Offers more practical examples plus an additional chapter dedicated to regression and data mining techniques and their limitations

* Uses resampling approach to introduction statistics

* A practical presentation that covers all three sampling methods: bootstrap, density-estimation, and permutations

* Includes systematic guide to help one select the correct procedure for a particular application

* Detailed coverage of all three statistical methodologies: classification, estimation, and hypothesis testing

* Suitable for classroom use and individual, self-study purposes

* Numerous practical examples using popular computer programs such as SAS®, Stata®, and StatXact®

* Useful appendixes with computer programs and code to develop individualized methods

* Downloadable freeware from author’s website: http://users.oco.net/drphilgood/resamp.htm

With its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of the bootstrap, cross-validation, and permutation tests. Students, professionals, and researchers will find it a prarticularly useful handbook for modern resampling methods and their applications.

Keywords

Applied Science Descriptive statistics Excel Industrial Statistics Nonparametric Statistics Permutation Resampling STATISTICA Stata algebra classification data analysis data mining ksa linear optimization

Authors and affiliations

  1. 1.Huntington BeachUSA

Bibliographic information

Industry Sectors
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Aerospace

Reviews

"[The book] has a 309-item bibliography, a glossary, and author and subject indices.… [It] provides much greater depth on the methods [than other books on the same subject]. Software support is broad." —Pharmaceutical Statistics

"I enjoyed this book. The style of writing suggests that statistics is fun and exploratory (which it often is). The reader is helped and encouraged to understand the problem (how the data were obtained) and how they might analyze it using resampling methods." —The American Statistician (review of the second edition)

"...the author has packaged an excellent and modern set of topics around the development and use of quantitative models.... If you need to learn about resampling, this book would be a good place to start." —Technometrics (review of the second edition)

"This book is what the title suggests, a practical guide to data analysis…helpful to the beginner…and a good resource…to an advanced user… The first few chapters provide a great pedagogical tool for creating in high school students an early interest in statistics. The book also proves to be a good resource for advanced topics with an extensive bibliography [and] provides in the Appendices C++, GAUSS, SAS, S-Plus and Stata codes for various procedures used in the book, along with resource material on resampling software." —Technometrics (review of the first edition)

"Readers with a wide variety of backgrounds and interests should find this book illuminating and a valuable introduction." —Short Book Reviews (Int’l Statistical Institute, review of the first edition)

"There is a list of 409 bibliographical references ranging from extremely theoretical to very applied… The appendices…contain advice for the ad hoc programmer, examples of code in various programming environments, as well as a guide to many resources for resampling software… More than 130 exercises are also included… This book has many very useful features for newcomers to applied statistics who will be able to learn a lot from it about the general principles of the business and about modern computer intensive methods." —Mathematical Reviews (review of the first edition)

"Most introductory statistics books ignore or give little attention to resampling methods, and thus another generation learns the less than optimal methods of statistical analysis. The author attempts to remedy this situation by writing an introductory text that focuses on resampling methods, and he does it well." —Ron. C. Fryxell, Albion College (review of the first edition)

"...The wealth of the bibliography covers a wide range of disciplines." —Dr. Dimitris Karlis, Athens University of Economics (review of the first edition)