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Introduction to Linear Regression Analysis, 4th Edition
Douglas C. Montgomery,
Arizona State Univ.
Elizabeth A. Peck,
The Coca-Cola Company
G. Geoffrey Vining,
Virginia Polytechnic and State Univ.
ISBN: 978-0-471-75495-4
©2006
640 pages
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Description
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Clearly balancing theory with applications, this book describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation. Topics not usually found in a linear regression textbook, such as nonlinear regression and generalized linear models, yet critical to engineering students and professionals, have also been included. The new critical role of the computer in regression analysis is reflected in the book's expanded discussion of regression diagnostics, where major analytical procedures now available in contemporary software packages, such as SAS, Minitab, and S-Plus, are detailed. The Appendix includes ample background material on the theory of linear models underlying regression analysis. Data sets from the book, extensive problem solutions, software hints and power point slides are available on an .ftp site ftp://ftp.wiley.com/public/sci_tech_med/linear_regression/
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