Applied Multivariate Statistics with SAS Software, Second Edition

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SAS Institute, 03.02.2000 - 368 Seiten
Real-world problems and data sets are the backbone of Ravindra Khattree and Dayanand Naik's Applied Multivariate Statistics with SAS Software, Second Edition, which provides a unique approach to the topic, integrating statistical methods, data analysis, and applications. Now extensively revised, the book includes new information about mixed effects models, applications of the MIXED procedure, regression diagnostics with the corresponding IML procedure code, and covariance structures. The authors' approach to the information will aid professors, researchers, and students in a variety of disciplines and industries. Extensive SAS code and the corresponding high-resolution output accompany sample problems, and clear explanations of SAS procedures are included. Emphasis is on correct interpretation of the output to draw meaningful conclusions. Featuring both the theoretical and the practical, topics covered include multivariate analysis of experimental data and repeated measures data, graphical representation of data including biplots, and multivariate regression. In addition, a quick introduction to the IML procedure with special reference to multivariate data is available in an appendix. SAS programs and output integrated with the text make it easy to read and follow the examples.

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Autoren-Profil (2000)

Ravindra Khattree, professor of applied statistics at Oakland University, Rochester,Michigan, received his graduate training at the Indian Statistical Institute in Calcutta.He received his Ph.D. at the University of Pittsburgh in 1985. He is an author or coauthor of numerous research papers on theoretical and applied statistics in various national and international journals and conference proceedings. His research interests include multivariate analysis, experimental designs, quality control, repeated measures, and statistical inference. In addition to teaching graduate and undergraduate courses, Dr. Khattree regularly consults with industry and academic researchers on various applied statistics problems. He is also an associate editor of Communications in Statistics and an editor of InterStat, a statistics journal on the Internet.

Dayanand N. Naik is an associate professor of statistics at Old Dominion University, Norfolk, Virginia. He received his M.S. degree in statistics from Karnatak University in Dharwad, India, and a Ph.D. degree in statistics from the University of Pittsburgh in 1985. He has published his research in several well-known journals, and he is the thesis advisor for many graduate students. His research and teaching interests include multivariate analysis, linear models, quality control, regression diagnostics, repeated measures analysis, and growth curve models. Dr. Naik is also an editor of InterStat, a statistics journal on the Internet.

 

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