Introduction to Linear Regression Analysis 5th Edition


Introduction to Linear Regression Analysis 5th Edition
by:Douglas C. Montgomery , Elizabeth A. Peck , G. Geoffrey Vining
Publisher Finelybook 出版社:Wiley; 5th edition (April 9, 2012)
Language 语言:English
pages 页数:672 pages
ISBN-10 书号:0470542810
ISBN-13 书号:9780470542811

Book Description
Editorial Reviews
Review
“The book can be used for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. It also serves as a resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.” (Zentralblatt MATH, 1 October 2013)

From the Inside Flap
Praise for the Fourth Edition

“As with previous editions, the authors have produced a leading textbook on regression.”
—Journal of the American Statistical Association

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

Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today’s cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences.

Following 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 then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics, including:

A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression models
Regression models with random effects in addition to a discussion on subsampling and the importance of the mixed model
Tests on individual regression coefficients and subsets of coefficients
Examples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data
In addition to Minitab®, SAS®, and S-PLUS®, the authors have incorporated JMP® and the freely available R software to illustrate the discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers to test their understanding of the material, and a related FTP site features the presented data sets, extensive problem solutions, software hints, and PowerPoint® slides to facilitate instructional use of the book.

Introduction to Linear Regression Analysis, Fifth Edition is an excellent book for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. The book also serves as a valuable, robust resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.

From the Back Cover
Praise for the Fourth Edition

“As with previous editions, the authors have produced a leading textbook on regression.”
―Journal of the American Statistical Association

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

Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today’s cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences.

Following 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 then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics, including:

A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression models
Regression models with random effects in addition to a discussion on subsampling and the importance of the mixed model
Tests on individual regression coefficients and subsets of coefficients
Examples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data
In addition to Minitab®, SAS®, and S-PLUS®, the authors have incorporated JMP® and the freely available R software to illustrate the discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers to test their understanding of the material, and a related FTP site features the presented data sets, extensive problem solutions, software hints, and PowerPoint® slides to facilitate instructional use of the book.

Introduction to Linear Regression Analysis, Fifth Edition is an excellent book for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. The book also serves as a valuable, robust resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.

About the Author
DOUGLAS C. MONTGOMERY, PHD, is Regents Professor of Industrial Engineering and Statistics at Arizona State University. Dr. Montgomery is a Fellow of the American Statistical Association, the American Society for Quality, the Royal Statistical Society, and the Institute of Industrial Engineers and has more than thirty years of academic and consulting experience. He has devoted his research to engineering statistics, specifically the design and analysis of experiments, statistical methods for process monitoring and optimization, and the analysis of time-oriented data. Dr. Montgomery is the coauthor of Generalized Linear Models:With Applications in Engineering and the Sciences, Second Edition and Introduction to Time Series Analysis and Forecasting, both published by:::::::::::::Wiley.

ELIZABETH A. PECK, PHD, is Logistics Modeling Specialist at the Coca-Cola Company in Atlanta, Georgia.

G. GEOFFREY VINING, PHD, is Professor in the Department of Statistics at Virginia Polytechnic and State University. He has published extensively in his areas of research interest, which include experimental design and analysis for quality improvement, response surface methodology, and statistical process control. A Fellow of the American Statistical Association and the American Society for Quality, Dr. Vining is the coauthor of Generalized Linear Models:With Applications in Engineering and the Sciences, Second Edition (Wiley).

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