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### 1st Edition

What are VitalSource eBooks? For Instructors Request Inspection Copy. Nonparametric Statistical Tests: A Computational Approach describes classical nonparametric tests, as well as novel and little-known methods such as the Baumgartner-Weiss-Schindler and the Cucconi tests.

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- nonparametric statistical الاختبارات: A computational ْ: Markus Neuhauser: dulogardapo.gq: Amazon US;

The book presents SAS and R programs, allowing readers to carry out the different statistical methods, such as permutation and bootstrap tests. The author considers example data sets in each chapter to illustrate methods. Numerous real-life data from various areas, including the bible, and their analyses provide for greatly diversified reading.

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Although the major development of nonparametric methods came to a certain end in the s, their importance undoubtedly persists. What is still needed is a computer assisted evaluation of their main properties. This book closes that gap.

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Introduction and Overview Nonparametric tests for the location problem Tests in case of heteroscedasticity Tests for the general alternative Ordered categorical and discrete data The conservativeness of permutation tests Further examples for the comparison of two groups One-sample tests and tests for paired data Tests for more than two groups Independence and correlation Stratified studies and combination of p-values Estimation and confidence intervals Appendix.

Nonparametric tests in R References. Highly recommended to anyone familiar with the classic nonparametric tests who wants an update and extensive bibliography concerning recent results. We provide complimentary e-inspection copies of primary textbooks to instructors considering our books for course adoption. Most VitalSource eBooks are available in a reflowable EPUB format which allows you to resize text to suit you and enables other accessibility features. Where the content of the eBook requires a specific layout, or contains maths or other special characters, the eBook will be available in PDF PBK format, which cannot be reflowed.

For both formats the functionality available will depend on how you access the ebook via Bookshelf Online in your browser or via the Bookshelf app on your PC or mobile device. Stay on CRCPress. Preview this Book. In some chapters a number of tests are simply given with an example and no guide as to what the best approach might be. Many formulae are given but little in the way of derivations because the issue is usually how well proposed tests work in practice rather than their theoretical justification.

The writing is clear and concise with only an occasional clue that English might not have been the original language. There are 24 pages of references at the end of the work for anyone who wishes to explore the material in greater depth. There are more references to works from Germany than we usually see, which adds to our knowledge base.

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The work is more free than most recent works from the kinds of errors spell checkers do not find. Highly recommended to anyone familiar with the classic nonparametric tests who wants an update and extensive bibliography concerning recent results. After a few years in industry, Robert W. Hayden bob statland. In he retired from full-time classroom work.

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## Buy Nonparametric Statistical Tests: A Computational Approach 2011

He now teaches statistics online at statistics. He contributed the chapter on evaluating introductory statistics textbooks to the MAA's Teaching Statistics. Introduction and Overview Nonparametric tests for the location problem Tests in case of heteroscedasticity Tests for the general alternative Ordered categorical and discrete data The conservativeness of permutation tests Further examples for the comparison of two groups One-sample tests and tests for paired data Tests for more than two groups Independence and correlation Stratified studies and combination of p-values Estimation and confidence intervals Appendix.

Nonparametric tests in R References. Skip to main content.

## 9781439867037 - Nonparametric Statistical Tests: a Computational Approach by Markus Neuhauser

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