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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 include the connections with bioequivalence, drug stability, and toxicity studies Real data sets analyzed by computer software packages illustrate the applications presented.
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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.About the Author:
Jason C. Hsu is a Professor in the Department of Statistics at The Ohio State University.
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