Multiple Comparisons

This book PDF is perfect for those who love Mathematics genre, written by Jason Hsu and published by CRC Press which was released on 01 February 1996 with total hardcover pages 292. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Multiple Comparisons books below.

Multiple Comparisons
Multiple Comparisons
Author : Jason Hsu
File Size : 47,9 Mb
Publisher : CRC Press
Language : English
Release Date : 01 February 1996
ISBN : 9781482221275
Pages : 292 pages
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Multiple Comparisons by Jason Hsu Book PDF Summary

Multiple Comparisons introduces simultaneous statistical inference and covers the theory and techniques for all-pairwise comparisons, multiple comparisons with the best, and multiple comparisons with a control. The author describes confidence intervals methods and stepwise exposes abuses and misconceptions, and guides readers to the correct method

Multiple Comparisons

Multiple Comparisons by Jason Hsu

Multiple Comparisons introduces simultaneous statistical inference and covers the theory and techniques for all-pairwise comparisons, multiple comparisons with the best, and multiple comparisons with a control. The author describes confidence intervals methods and stepwise exposes abuses and misconceptions, and guides readers to the correct method

Get Book
Multiple Comparison Procedures

Multiple Comparison Procedures by Larry E. Toothaker

If you conduct research with more than two groups and want to find out if they are significantly different when compared two at a time, then you need Multiple Comparison Procedures. Using examples to illustrate major concepts, this concise volume is your guide to multiple comparisons. Toothaker thoroughly explains such

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Pairwise Multiple Comparisons

Pairwise Multiple Comparisons by Taka-aki Shiraishi,Hiroshi Sugiura,Shin-ichi Matsuda

This book focuses on all-pairwise multiple comparisons of means in multi-sample models, introducing closed testing procedures based on maximum absolute values of some two-sample t-test statistics and on F-test statistics in homoscedastic multi-sample models. It shows that (1) the multi-step procedures are more powerful than single-step procedures and the Ryan/Einot–

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Multiple Comparisons Using R

Multiple Comparisons Using R by Frank Bretz,Torsten Hothorn,Peter Westfall

Adopting a unifying theme based on maximum statistics, Multiple Comparisons Using R describes the common underlying theory of multiple comparison procedures through numerous examples. It also presents a detailed description of available software implementations in R. The R packages and source code for the analyses are available at http://CRAN.

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Multiple Comparison Procedures

Multiple Comparison Procedures by Yosef Hochberg,Ajit C. Tamhane

Offering a balanced, up-to-date view of multiple comparison procedures, this book refutes the belief held by some statisticians that such procedures have no place in data analysis. With equal emphasis on theory and applications, it establishes the advantages of multiple comparison techniques in reducing error rates and in ensuring the

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Multiple Comparisons

Multiple Comparisons by Jason C. Hsu

Multiple Comparisons introduces simultaneous statistical inference and covers the theory and techniques for all-pairwise comparisons, multiple comparisons with the best, and multiple comparisons with a control. The author describes confidence intervals methods and stepwise exposes abuses and misconceptions, and guides readers to the correct method for each problem. Discussions also

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Multiple Comparisons for Researchers

Multiple Comparisons for Researchers by Larry E. Toothaker

Through clear exposition and step-by-step procedures, Toothaker describes all the most important multiple comparison procedures along with relevant concepts, such as error rate, power, robustness and coverage of two-way ANOVA including the controversy on cell mean versus tests on interaction effects. The book also includes samples of multiple comparison programs

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Learning Statistics with R

Learning Statistics with R by Daniel Navarro

"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data

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