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
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Praise for the First Edition

"The obvious enthusiasm of Myers, Montgomery, and Vining andtheir reliance on their many examples as a major focus of theirpedagogy make Generalized Linear Models a joy to read. Everystatistician working in any area of applied science should buy itand experience the excitement of these new approaches to familiaractivities."
—Technometrics

Generalized Linear Models: With Applications in Engineeringand the Sciences, Second Edition continues to provide a clearintroduction to the theoretical foundations and key applications ofgeneralized linear models (GLMs). Maintaining the same nontechnicalapproach as its predecessor, this update has been thoroughlyextended to include the latest developments, relevant computationalapproaches, and modern examples from the fields of engineering andphysical sciences.

This new edition maintains its accessible approach to the topicby reviewing the various types of problems that support the use ofGLMs and providing an overview of the basic, related concepts suchas multiple linear regression, nonlinear regression, least squares,and the maximum likelihood estimation procedure. Incorporating thelatest developments, new features of this Second Editioninclude:

  • 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 andordinal responses, and overdispersion

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

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

  • Illustrations of R code to perform GLM analysis

The authors demonstrate the diverse applications of GLMs throughnumerous examples, from classical applications in the fields ofbiology and biopharmaceuticals to more modern examples related toengineering and quality assurance. The Second Edition hasbeen designed to demonstrate the growing computational nature ofGLMs, as SAS®, Minitab®, JMP®, and R softwarepackages are used throughout the book to demonstrate fitting andanalysis of generalized linear models, perform inference, andconduct diagnostic checking. Numerous figures and screen shotsillustrating computer output are provided, and a related FTP sitehouses supplementary material, including computer commands andadditional data sets.

Generalized Linear Models, Second Edition is an excellentbook for courses on regression analysis and regression modeling atthe upper-undergraduate and graduate level. It also serves as avaluable reference for engineers, scientists, and statisticians whomust 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 theDepartment of Statistics at Virginia Polytechnic Institute andState University. He has more than forty years of academicexperience in the areas of experimental design and analysis,response surface analysis, and designs for nonlinear models. AFellow of the American Statistical Society, Dr. Myers is thecoauthor of numerous books including Response SurfaceMethodology: Process and Product Optimization Using DesignedExperiments, Third Edition (Wiley).

Douglas C. Montgomery, PhD, is Regents' Professor ofIndustrial Engineering and Statistics at Arizona State University.Dr. Montgomery has more than thirty years of academic andconsulting experience and has devoted his research to engineeringstatistics, specifically the design and analysis of experiments. Hehas authored or coauthored numerous journal articles and twelvebooks, including Response Surface Methodology: Process andProduct Optimization Using Designed Experiments, Third Edition;Introduction to Linear Regression Analysis, Fourth Edition; andIntroduction to Time Series Analysis and Forecasting, allpublished by Wiley.

G. Geoffrey Vining, PhD, is Professor in the Departmentof Statistics at Virginia Polytechnic Institute and StateUniversity. A Fellow of both the American Statistical Associationand the American Society for Quality, Dr. Vining is also thecoauthor of Introduction to Linear Regression Analysis, FourthEdition (Wiley).

Timothy J. Robinson, PhD, is Associate Professor in theDepartment of Statistics at the University of Wyoming. He haswritten numerous journal articles in the areas of design ofexperiments, response surface methodology, and applications ofcategorical data analysis in engineering, medicine, and theenvironmental sciences.

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9780471355731: Generalized Linear Models: With Applications in Engineering and the Sciences

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