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Rank and Pseudo-Rank Procedures for Independent Observations in Factorial Designs: Using R and SAS (Springer Series in Statistics)
Book 153 of 160: Springer Series in StatisticsSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 117.36
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In English.
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Condition: New. 1st ed. 2018 edition NO-PA16APR2015-KAP.
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Hardcover. Condition: Brand New. 521 pages. 9.25x6.25x1.25 inches. In Stock.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explains how to analyze independent data from factorial designs without having to make restrictive assumptions, such asnormality of the data, or equal variances. The general approach also allows for ordinal and even dichotomous data. The underlying effect size is the nonparametric relative effect, which has a simple and intuitive probability interpretation. The data analysis is presented as comprehensively as possible, including appropriate descriptive statistics which follow a nonparametric paradigm, as well as corresponding inferential methods using hypothesis tests and confidence intervals based on pseudo-ranks.Offering clear explanations, an overview of the modern rank- and pseudo-rank-based inference methodology and numerous illustrations with real data examples, as well as the necessary R/SAS code to run the statistical analyses, this book is a valuable resource for statisticians and practitioners alike.
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Rank- and Pseudo-Rank Procedures for Independent Observations in Factorial Designs
Book 153 of 160: Springer Series in StatisticsLanguage: English
Published by Springer International Publishing, 2019
ISBN 10: 3030029123 ISBN 13: 9783030029128
Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers a detailed overview of the modern rank- and pseudo-rank-based inference methodologyRichly illustrated with examples Provides codes (R, SAS) for the statististical analysis .
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