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Nonparametric Hypothesis Testing – Rank and Permutation Methods with Applications in R Hardback - 2014
by Bonnini, Stefano/ Corain, Livio/ Marozzi, Marco/ Salmaso, Luigi
- New
- Hardback
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Details
- Title Nonparametric Hypothesis Testing – Rank and Permutation Methods with Applications in R
- Author Bonnini, Stefano/ Corain, Livio/ Marozzi, Marco/ Salmaso, Luigi
- Binding Hardback
- Condition New
- Pages 256
- Volumes 1
- Language ENG
- Publisher John Wiley & Sons Inc
- Publication date 2014
- Illustrated Yes
- Features Bibliography, Illustrated, Index, Table of Contents
- Bookseller's Inventory # __1119952379
- ISBN 9781119952374 / 1119952379
- Weight 1.05 lbs (0.48 kg)
- Dimensions 9.1 x 6.1 x 0.7 in (23.11 x 15.49 x 1.78 cm)
- Category Mathematics
- Library of Congress subjects Nonparametric statistics, Statistical hypothesis testing
- Library of Congress Catalogue Number 2014020574
- Dewey Decimal Code 519.54
- Quantity available 1
About Revaluation Books Devon, United Kingdom
Biblio member since 2020
General bookseller of both fiction and non-fiction.
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From the rear cover
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.
