Nonparametric Hypothesis Testing

Rank and Permutation Methods with Applications in R

Nonfiction, Science & Nature, Mathematics, Statistics
Cover of the book Nonparametric Hypothesis Testing by Stefano Bonnini, Livio Corain, Marco Marozzi, Luigi Salmaso, Wiley
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Author: Stefano Bonnini, Livio Corain, Marco Marozzi, Luigi Salmaso ISBN: 9781118763483
Publisher: Wiley Publication: July 1, 2014
Imprint: Wiley Language: English
Author: Stefano Bonnini, Livio Corain, Marco Marozzi, Luigi Salmaso
ISBN: 9781118763483
Publisher: Wiley
Publication: July 1, 2014
Imprint: Wiley
Language: English

A novel presentation of rank and permutation tests, with accessible guidance to applications in R

Nonparametric testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. This book summarizes traditional rank techniques and more recent developments in permutation testing as robust tools for dealing with complex data with low sample size.

Key Features:

  • Examines the most widely used methodologies of nonparametric testing.
  • Includes extensive software codes in R featuring worked examples, and uses real case studies from both experimental and observational studies.
  • Presents and discusses solutions to the most important and frequently encountered real problems in different fields.

Features a supporting website (www.wiley.com/go/hypothesis_testing) containing all of the data sets examined in the book along with ready to use R software codes.

Nonparametric Hypothesis Testing combines an up to date overview with useful practical guidance to applications in R, and will be a valuable resource for practitioners and researchers working in a wide range of scientific fields including engineering, biostatistics, psychology and medicine.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

A novel presentation of rank and permutation tests, with accessible guidance to applications in R

Nonparametric testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. This book summarizes traditional rank techniques and more recent developments in permutation testing as robust tools for dealing with complex data with low sample size.

Key Features:

Features a supporting website (www.wiley.com/go/hypothesis_testing) containing all of the data sets examined in the book along with ready to use R software codes.

Nonparametric Hypothesis Testing combines an up to date overview with useful practical guidance to applications in R, and will be a valuable resource for practitioners and researchers working in a wide range of scientific fields including engineering, biostatistics, psychology and medicine.

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