Generalized Linear Models

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.