Introduction to Linear Regression Analysis 5th Edition by Douglas C. Montgomery (PDF)

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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 AssociationA comprehensive and up-to-date introduction to the fundamentals of regression analysisIntroduction 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 modelsRegression models with random effects in addition to a discussion on subsampling and the importance of the mixed modelTests on individual regression coefficients and subsets of coefficientsExamples 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.

User’s Reviews

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 analysisIntroduction 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 modelsRegression models with random effects in addition to a discussion on subsampling and the importance of the mixed modelTests on individual regression coefficients and subsets of coefficientsExamples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction dataIn 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 analysisIntroduction 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 modelsRegression models with random effects in addition to a discussion on subsampling and the importance of the mixed modelTests on individual regression coefficients and subsets of coefficientsExamples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction dataIn 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). Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Unfortunately despite its visual appeal and ample detail, there are many typos. The typos are pretty obvious and mostly just incorrect notation (wrong subscript or missing symbols) so it is not incomprehensible, but they are widespread.On the bright side, the explanations and examples are good. I used this book for a masters level statistics course.

⭐The content of the book is fine. The physical quality of the book was less than perfect. The book would have been in pristine, new condition except that the binding was poorly finished. First, the bound pages were not centered in the cover. The top of the pages were mounted flush with the upper edge of the cover. Additionally, the pages weren’t securely glued into the cover and separated from the front of the cover. I suppose I can reglue it myself but wanted to point out that the finishing quality by the publisher was sub par.

⭐No false advertising here – it is a textbook for Montgomery’s Regression Analysis class @ ASU IEE572. The book arrived in good time and in pristine condition.

⭐If you want to trully understand Linear Regression this is your book.I am coursing a Masters degree in statistics and this has been really useful to understand what the teacher teaches in class.If you just want to see every command related to linear regression available on known software as a black box (trust me, software is going to provide a result, the thing is what you do with it) don’t read this book, just google a few examples.

⭐This is a very good book, and it’s easy to understand. My only complaint is that a piece of the cover was torn upon arrival. I’m not sure if it’s a shipping issue, or it was sent that way. I recommend this book.

⭐The content is good but my three star rating is based solely on the format and user experience of the ebook. I would strongly recommend avoiding the e book and purchasing the hard copy

⭐Have a three star because it has markings all over the book. Easy read if you understand what’s going on.

⭐Required textbook for a class. It is fine.

⭐While there is a newer edition of this book available, the content of this edition is still relevant. This is an excellent book for data analysts in-training as well as students learning about linear regression.

⭐Excelente libro, muchos ejercicios y no es difícil de seguir

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⭐El libro no tenía plástico protector, algunas hojas venían un poco dobladas. El contenido es genial, muy actualizado y didáctico.

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⭐Excelente guía para un análisis completo y fiable de la regresión lineal con criterios estadísticos y varias metodologías para adecuarla.

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⭐線型回帰分析に関する必要な事項が網羅されている良書です。英語も平易、例も豊富です。理論と実践双方バランスよくカバーされています。統計解析を実務でされている方すべてにお勧めできます。一般化線型モデル（GML）については一章割かれていますが、これはイントロ程度ですので、同書にも紹介されているように、他書の参照が必要と思われます。

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