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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.
...[the authors] describe conventional uses of the technique, as well as less common ones, placing linear regression in the practical context of today's mathematical and scientific research.
New edition of a text on regression analysis, a statistical technique for investigating and modeling the relationship between variables. Montgomery (industrial engineering, Arizona State U.), Elizabeth A. Peck (logistics modeling specialist, Coca-Cola Co.) and G. Geoffrey Vining (statistics, Virginia Tech) describe conventional uses of the technique, as well as less common ones, placing linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction, they outline a host of technical tools including basic inference procedures and introductory aspects of model adequacy checking, simple and multiple linear regression, model adequacy checking, transformations and weighting to correct model inadequacies, diagnostics for leverage and influence, polynomial regression models, indicator variables, variable selection and model building, multicollinearity, robust regression, generalized linear models, nonlinear regression, validation of regression models, and other topics. Annotation c. Book News, Inc., Portland, OR (booknews.com)
DOUGLAS C. MONTGOMERY is ASU Foundation Professor of Engineering and Professor of Statistics at Arizona State University.
ELIZABETH A. PECK is Logistics Modeling Specialist at the Coca-Cola Company in Atlanta, Georgia.
G. GEOFFREY VINING is Professor and Head of the Department of Statistics at Virginia Polytechnic Institute and State University. All three authors have published extensively in both journals and books.