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Introduction to Linear Regression Analysis, by Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining
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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.
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.
- Sales Rank: #73741 in Books
- Brand: Brand: Wiley
- Published on: 2012-04-09
- Original language: English
- Number of items: 1
- Dimensions: 10.30" h x 1.80" w x 7.40" l, 2.77 pounds
- Binding: Hardcover
- 672 pages
- Used Book in Good Condition
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 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).
Most helpful customer reviews
3 of 3 people found the following review helpful.
Great descriptions and love the code samples!
By Michelle A. Sisneros
This book is required for my regression course. I love it so far! The concepts are described well and I like that it has example SAS and R code throughout. I've only just started using the text, so I can update when I've read more than the first 2 chapters.
11 of 15 people found the following review helpful.
Good, but could be better
By Aerospace Engineer
Just started a course using this book. I have 30 years in R&D and wanted to increase my knowledge. On the one hand the book is pretty good at showing the theory but to me the text could be a little clearer. Beyond that the thing it most needs are more real world, worked out case studies. It's one thing to show an example of take this data, do that thing to it and voila, vs telling me why I should take that data, why I should do that thing instead of some other thing, and interpret what it means. The book too quickly jumps into higher level concerns before establishing a good grounding in fundamentals. In that respect it has much in common with other stats books I've used.
2 of 2 people found the following review helpful.
Really great book
By Joshua Bjorg Keeling
Great book. Well written and very comprehensive. Contains examples in R and SAS along with datasets available online. Best regression book I've read yet.
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