Multiple Comparisons - Info and Reading Options
Theory and Methods
By Jason Hsu
"Multiple Comparisons" was published by Chapman & Hall/CRC in February 1, 1996, it has 277 pages and the language of the book is English.
“Multiple Comparisons” Metadata:
- Title: Multiple Comparisons
- Author: Jason Hsu
- Language: English
- Number of Pages: 277
- Publisher: Chapman & Hall/CRC
- Publish Date: February 1, 1996
“Multiple Comparisons” Subjects and Themes:
- Subjects: ➤ Correlation multiple (Statistique) - Statistics - Multiple comparisons (Statistics) - Sequentiele analyse (statistiek) - Estatistica - Multipler Mittelwertvergleich - Experimental design - Analysis of variance - Corrélation multiple (Statistique) - MATHEMATICS - Applied - Probability & Statistics - General - Sequentiële analyse (statistiek)
Edition Identifiers:
- The Open Library ID: OL7479285M - OL8088307W
- Online Computer Library Center (OCLC) ID: 883391078 - 35042807
- Library of Congress Control Number (LCCN): 95072199
- ISBN-13: 9780412982811
- ISBN-10: 0412982811
- All ISBNs: 0412982811 - 9780412982811
AI-generated Review of “Multiple Comparisons”:
Snippets and Summary:
This chapter discusses simultaneous statistical inference, that is, inference on several parameters at once, in a setting simpler than multiple comparisons inference, which is simultaneous inference on certain functions of the differences of the treatment effects.
"Multiple Comparisons" Description:
The Open Library:
Multiple comparisons are the comparisons of two or more treatments. These may be treatments of a disease, groups of subjects, or computer systems, for example. Statistical multiple comparison methods are used heavily in research, education, business, and manufacture to analyze data, but are often used incorrectly. This book exposes such abuses and misconceptions, and guides the reader to the correct method of analysis for each problem. Theories for all-pairwise comparisons, multiple comparison with the best, and multiple comparison with a control are discussed, and methods giving statistical inference in terms of confidence intervals, confident directions, and confident inequalities are described. Applications are illustrated with real data. Included are recent methods empowered by modern computers. Multiple Comparisons will be valued by researchers and graduate students interested in the theory of multiple comparisons, as well as those involved in data analysis in biological and social sciences, medicine, business and engineering. It will also interest professional and consulting statisticians in the pharmaceutical industry, and quality control engineers in manufacturing companies.
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