Generalized Linear Models: with Applications in Engineering and the Sciences 2nd Edition by Raymond H. Myers (PDF)

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Praise for the First Edition”The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities.” ―TechnometricsGeneralized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Maintaining the same nontechnical approach as its predecessor, this update has been thoroughly extended to include the latest developments, relevant computational approaches, and modern examples from the fields of engineering and physical sciences.This new edition maintains its accessible approach to the topic by reviewing the various types of problems that support the use of GLMs and providing an overview of the basic, related concepts such as multiple linear regression, nonlinear regression, least squares, and the maximum likelihood estimation procedure. Incorporating the latest developments, new features of this Second Edition include:A new chapter on random effects and designs for GLMsA thoroughly revised chapter on logistic and Poisson regression, now with additional results on goodness of fit testing, nominal and ordinal responses, and overdispersionA new emphasis on GLM design, with added sections on designs for regression models and optimal designs for nonlinear regression modelsExpanded discussion of weighted least squares, including examples that illustrate how to estimate the weightsIllustrations of R code to perform GLM analysisThe authors demonstrate the diverse applications of GLMs through numerous examples, from classical applications in the fields of biology and biopharmaceuticals to more modern examples related to engineering and quality assurance. The Second Edition has been designed to demonstrate the growing computational nature of GLMs, as SAS®, Minitab®, JMP®, and R software packages are used throughout the book to demonstrate fitting and analysis of generalized linear models, perform inference, and conduct diagnostic checking. Numerous figures and screen shots illustrating computer output are provided, and a related FTP site houses supplementary material, including computer commands and additional data sets.Generalized Linear Models, Second Edition is an excellent book for courses on regression analysis and regression modeling at the upper-undergraduate and graduate level. It also serves as a valuable reference for engineers, scientists, and statisticians who must understand and apply GLMs in their work.

User’s Reviews

Editorial Reviews: Review “Generalized linear models, second edition, is an excellent book for courses on regression analysis and regression modeling at the upper-undergraduate and graduate levels. It also serves as a valuable reference for engineers, scientists, and statisticians who must understand and apply GLMs in their work.” (Mathematical Reviews, 2011) From the Inside Flap Praise for the First Edition”The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities.” —TechnometricsGeneralized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Maintaining the same nontechnical approach as its predecessor, this update has been thoroughly extended to include the latest developments, relevant computational approaches, and modern examples from the fields of engineering and physical sciences.This new edition maintains its accessible approach to the topic by reviewing the various types of problems that support the use of GLMs and providing an overview of the basic, related concepts such as multiple linear regression, nonlinear regression, least squares, and the maximum likelihood estimation procedure. Incorporating the latest developments, new features of this Second Edition include:A new chapter on random effects and designs for GLMsA thoroughly revised chapter on logistic and Poisson regression, now with additional results on goodness of fit testing, nominal and ordinal responses, and overdispersionA new emphasis on GLM design, with added sections on designs for regression models and optimal designs for nonlinear regression modelsExpanded discussion of weighted least squares, including examples that illustrate how to estimate the weightsIllustrations of R code to perform GLM analysisThe authors demonstrate the diverse applications of GLMs through numerous examples, from classical applications in the fields of biology and biopharmaceuticals to more modern examples related to engineering and quality assurance. The Second Edition has been designed to demonstrate the growing computational nature of GLMs, as SAS®, Minitab®, JMP®, and R software packages are used throughout the book to demonstrate fitting and analysis of generalized linear models, perform inference, and conduct diagnostic checking. Numerous figures and screen shots illustrating computer output are provided, and a related FTP site houses supplementary material, including computer commands and additional data sets.Generalized Linear Models, Second Edition is an excellent book for courses on regression analysis and regression modeling at the upper-undergraduate and graduate level. It also serves as a valuable reference for engineers, scientists, and statisticians who must understand and apply GLMs in their work. From the Back Cover Praise for the First Edition”The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities.” ―TechnometricsGeneralized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Maintaining the same nontechnical approach as its predecessor, this update has been thoroughly extended to include the latest developments, relevant computational approaches, and modern examples from the fields of engineering and physical sciences.This new edition maintains its accessible approach to the topic by reviewing the various types of problems that support the use of GLMs and providing an overview of the basic, related concepts such as multiple linear regression, nonlinear regression, least squares, and the maximum likelihood estimation procedure. Incorporating the latest developments, new features of this Second Edition include:A new chapter on random effects and designs for GLMsA thoroughly revised chapter on logistic and Poisson regression, now with additional results on goodness of fit testing, nominal and ordinal responses, and overdispersionA new emphasis on GLM design, with added sections on designs for regression models and optimal designs for nonlinear regression modelsExpanded discussion of weighted least squares, including examples that illustrate how to estimate the weightsIllustrations of R code to perform GLM analysisThe authors demonstrate the diverse applications of GLMs through numerous examples, from classical applications in the fields of biology and biopharmaceuticals to more modern examples related to engineering and quality assurance. The Second Edition has been designed to demonstrate the growing computational nature of GLMs, as SAS®, Minitab®, JMP®, and R software packages are used throughout the book to demonstrate fitting and analysis of generalized linear models, perform inference, and conduct diagnostic checking. Numerous figures and screen shots illustrating computer output are provided, and a related FTP site houses supplementary material, including computer commands and additional data sets.Generalized Linear Models, Second Edition is an excellent book for courses on regression analysis and regression modeling at the upper-undergraduate and graduate level. It also serves as a valuable reference for engineers, scientists, and statisticians who must understand and apply GLMs in their work. About the Author Raymond H. Myers, PhD, is Professor Emeritus in the Department of Statistics at Virginia Polytechnic Institute and State University. He has more than forty years of academic experience in the areas of experimental design and analysis, response surface analysis, and designs for nonlinear models. A Fellow of the American Statistical Society, Dr. Myers is the coauthor of numerous books including Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Third Edition (Wiley). Douglas C. Montgomery, PhD, is Regents’ Professor of Industrial Engineering and Statistics at Arizona State University. Dr. Montgomery has more than thirty years of academic and consulting experience and has devoted his research to engineering statistics, specifically the design and analysis of experiments. He has authored or coauthored numerous journal articles and twelve books, including Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Third Edition; Introduction to Linear Regression Analysis, Fourth Edition; and Introduction to Time Series Analysis and Forecasting, all published by Wiley.G. Geoffrey Vining, PhD, is Professor in the Department of Statistics at Virginia Polytechnic Institute and State University. A Fellow of both the American Statistical Association and the American Society for Quality, Dr. Vining is also the coauthor of Introduction to Linear Regression Analysis, Fourth Edition (Wiley).Timothy J. Robinson, PhD, is Associate Professor in the Department of Statistics at the University of Wyoming. He has written numerous journal articles in the areas of design of experiments, response surface methodology, and applications of categorical data analysis in engineering, medicine, and the environmental sciences. Read more

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

⭐This edition (2nd) of this excellent book has many typos and errors. I am very disappointed that so far, no errata are available online. I hope the errors will be corrected in future printings.

⭐I learnt from this book that’s OK if you’re a lousy researcher and not paying attention to details and write wrong stuff here and there. “Who Cares” is basically the attitude of the authors of this book. They relied on their reputation to get this thing pass. I read large portions of the book and have never witnessed this number of typos in one book in my whole life. Many of them are serious. I wasted so much time confused by these errors.Above all, it contains wrong results. For example, in page 227 ” For the gamma response case, it is appropriate to use the scaled deviance in the SAS output as a measure of the overall fit of the mode”. NO, IT IS TOTALLY INAPPROPRIATE FOR GAMMA FAMILY TO USE SUCH A MEASURE! I won’t go to the technical details but this number almost always will be close to one for gamma. Very misleading statement. Actually, if you miss up their model by deleting x1 and x2 you’ll get back 1.19 as compared to 1.17 using their model. There are some stuff like that across the book.Other points: 1. No discussion (two sentences maybe) of whatsoever about negative binomial regression (Very important one, see Hilbe book). They just introduced quasi -poission as an alterative to poisson. 2. Few pages dedicated to ordinal and nominal data 3. Estimation in GLMs is terribly introduced.I like the way they write and I can see myself enjoying the book if they have taken their readers seriously and worked harder on it.

⭐The book has good problems but a lot of typos. I haven’t seen so many typos in any other text book.

⭐This book is completely unreliable. It has hundreds of typographical and conceptual errors, including many errors in its formulas. When I became aware of all this book’s problems, I decided to use it only as a source of datasets for my GLM course, not as a textbook as I originally intended to. However, today I realized that one of its datasets has many errors too. Specifically, Table 6.1 corresponding to a “Respiratory Example” is completely different from the original data in Stokes, Davis and Koch (1995) book, which is cited as the source of the table. The book website provides a file with this table’s data, but the data in that file is completely different from the original data. My conclusion is that the four authors of this book did not take seriously the task of writing it.

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