Generalized Linear Models: with Applications in Engineering and the Sciences

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9780470454633: Generalized Linear Models: with Applications in Engineering and the Sciences

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."
—Technometrics

Generalized 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 GLMs

  • A thoroughly revised chapter on logistic and Poisson regression, now with additional results on goodness of fit testing, nominal and ordinal responses, and overdispersion

  • A new emphasis on GLM design, with added sections on designs for regression models and optimal designs for nonlinear regression models

  • Expanded discussion of weighted least squares, including examples that illustrate how to estimate the weights

  • Illustrations of R code to perform GLM analysis

The 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.

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From the Back Cover:

A comprehensive introduction to generalized linear models, including logistic and Poisson regression

This volume presents a thorough introductory treatment of generalized linear models (GLM). It features useful examples of GLMs at work in a variety of settings ranging from applications in biology and biopharmaceuticals, to engineering and quality assurance.

The authors review the types of problems that support the use of GLMs, then provide an overview of many of the basic concepts of multiple linear regression and nonlinear regression. Fundamental concepts such as least squares and the maximum likelihood estimation procedure are discussed. Confidence intervals estimation, hypothesis testing procedures, and model diagnostic checking techniques such as residual plotting and influence diagnostics are also presented in detail. Further coverage includes:
* Model fitting, inference, and diagnostics for nonlinear regression
* Coverage of logistic and Poisson regression
* Vivid illustrations of model fitting, inference, and diagnostic checking using SAS PROC GENMOD and S-PLUS software
* Introduction to generalized estimating equations (GEEs)

Generalized Linear Models provides an in-depth introduction to the subject for graduate students in regression courses and 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.

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