"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.

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nexusstc/Resampling Methods: A Practical Guide to Data Analysis/f4eaecb8177cd4bf0222307c553ab43a.pdf

zlib/no-category/Phillip I. Good (auth.)/Resampling Methods: A Practical Guide to Data Analysis_2104837.pdf

Phillip I. Good; Philip Good

Birkhauser Verlag

Springer Nature

Springer-Verlag

United States, United States of America

Springer Nature, Boston, MA, 2013

First edition, Boston, MA, 1999

1, 20130314

uuuu

lg950943

{"edition":"1","isbns":["1475730497","1475730519","9781475730494","9781475730517"],"last_page":269,"publisher":"Birkhäuser Boston"}

Front Matter....Pages i-xii
Descriptive Statistics....Pages 1-25
Cause and Effect....Pages 26-45
Testing Hypotheses....Pages 46-66
When the Distribution Is Known....Pages 67-76
Estimation....Pages 77-96
Power of a Test....Pages 97-112
Categorical Data....Pages 113-129
Experimental Design and Analysis....Pages 130-155
Multiple Variables and Multiple Hypotheses....Pages 156-163
Classification and Discrimination....Pages 164-186
Survival Analysis and Reliability....Pages 187-198
Which Statistic Should I Use?....Pages 199-207
Back Matter....Pages 208-269

2013-08-01