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1. |
Record Nr. |
UNINA9910332461203321 |
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Autore |
Brunner Edgar |
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Titolo |
Rank and Pseudo-Rank Procedures for Independent Observations in Factorial Designs : Using R and SAS / / by Edgar Brunner, Arne C. Bathke, Frank Konietschke |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
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ISBN |
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Edizione |
[1st ed. 2018.] |
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Descrizione fisica |
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1 online resource (535 pages) |
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Collana |
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Springer Series in Statistics, , 0172-7397 |
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Disciplina |
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Soggetti |
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Statistics |
Biometry |
Pharmaceutical technology |
R (Computer program language) |
Statistics for Life Sciences, Medicine, Health Sciences |
Biostatistics |
Pharmaceutical Sciences/Technology |
Statistical Theory and Methods |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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1 Types of Data and Designs -- 2 Distributions and Effects -- 3 Two Samples -- 4 Several Samples -- 5 Two-Factor Crossed Designs -- 6 Designs with Three and More Factors -- 7 Derivation of Main Results -- 8 Mathematical Techniques -- References -- A Software and Program Code -- B Data Sets and Descriptions -- Index. . |
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Sommario/riassunto |
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This book explains how to analyze independent data from factorial designs without having to make restrictive assumptions, such as normality 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 |
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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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