A comprehensive and up?to?date introduction to the fundamentals of regression analysis


The Fourth Edition of Introduction to Linear Regression Analysis describes both the conventional and less common uses of linear regression in the practical context of today?s mathematical and scientific research. This popular book blends both theory and application to equip the reader with an understanding of the basic principles necessary to apply regression model?building techniques in a wide variety of application environments. It assumes a working knowledge of basic statistics and a familiarity with hypothesis testing and confidence intervals, as well as the normal, t, x2, and F distributions.

Illustrating all of the major procedures employed by the contemporary software packages MINITAB(r), SAS(r), and S?PLUS(r), the Fourth Edition begins with a general introduction to regression modeling, including typical applications. A host of technical tools are outlined, such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. Subsequent chapters discuss:
? Indicator variables and the connection between regression and analysis?of?variance models
? Variable selection and model?building techniques and strategies
? The multicollinearity problem??its sources, effects, diagnostics, and remedial measures
? Robust regression techniques such as M?estimators, and properties of robust estimators
? The basics of nonlinear regression
? Generalized linear models
? Using SAS(r) for regression problems

This book is a robust resource that offers solid methodology for statistical practitioners and professionals in the fields of engineering, physical and chemical sciences, economics, management, life and biological sciences, and the social sciences. Both the accompanying FTP site, which contains data sets, extensive problem solutions, software hints, and PowerPoint(r) slides, as well as the book?s revised presentation of topics in increasing order of complexity, facilitate its use in a classroom setting.

With its new exercises and structure, this book is highly recommended for upper?undergraduate and beginning graduate students in mathematics, engineering, and natural sciences. Scientists and engineers will find the book to be an excellent choice for reference and self?study.

This is a comprehensive and up-to-date introduction to the fundamentals of regression analysis. "The Fourth Edition of Introduction to Linear Regression Analysis" describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. This popular book blends both theory and application to equip the reader with an understanding of the basic principles necessary to apply regression model-building techniques in a wide variety of application environments. It assumes a working knowledge of basic statistics and a familiarity with hypothesis testing and confidence intervals, as well as the normal, t, x2, and F distributions. Illustrating all of the major procedures employed by the contemporary software packages MINITAB[registered], SAS[registered], and S-PLUS[registered], the Fourth Edition begins with a general introduction to regression modeling, including typical applications. A host of technical tools are outlined, such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations.

The book discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. Subsequent chapters discuss: indicator variables and the connection between regression and analysis-of-variance models; variable selection and model-building techniques and strategies; the multicollinearity problem - its sources, effects, diagnostics, and remedial measures; robust regression techniques such as M-estimators, and properties of robust estimators; the basics of nonlinear regression; generalized linear models; and using SAS[registered] for regression problems. This book is a robust resource that offers solid methodology for statistical practitioners and professionals in the fields of engineering, physical and chemical sciences, economics, management, life and biological sciences, and the social sciences. Both the accompanying FTP site, which contains data sets, extensive problem solutions, software hints, and PowerPoint[registered] slides, as well as the book's revised presentation of topics in increasing order of complexity, facilitate its use in a classroom setting.

With its new exercises and structure, this book is highly recommended for upper-undergraduate and beginning graduate students in mathematics, engineering, and natural sciences. Scientists and engineers will find the book to be an excellent choice for reference and self-study.

A comprehensive and thoroughly up?to?date look at regression analysis—still the most widely used technique in statistics today

As basic to statistics as the Pythagorean theorem is to geometry, regression analysis is a statistical technique for investigating and modeling the relationship between variables. With far?reaching applications in almost every field, regression analysis is used in engineering, the physical and chemical sciences, economics, management, life and biological sciences, and the social sciences.

Clearly balancing theory with applications, Introduction to Linear Regression Analysis describes 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 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. Succeeding chapters include detailed coverage of:

• Indicator variables, making the connection between regression and analysis?of?variance modelss
• Variable selection and model?building techniques
• The multicollinearity problem, including its sources, harmful effects, diagnostics, and remedial measures
• Robust regression techniques, including M?estimators, Least Median of Squares, and S?estimation
• Generalized linear models

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 now includes ample background material on the theory of linear models underlying regression analysis. Data sets from the book, extensive problem solutions, and software hints are available on the ftp site. For other Wiley books by Doug Montgomery, visit our website at www.wiley.com/college/montgomery.

DOUGLAS C. MONTGOMERY is Professor in the Department of Industrial Engineering, 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, Virginia Tech.