Generalized Linear Models | Zookal Textbooks | Zookal Textbooks
  • Author(s) Raymond H. Myers / Douglas C. Montgomery / G. Geoffrey Vining / Timothy J. Robinson
  • Subtitlewith Applications in Engineering and the Sciences
  • Edition1
  • Published4th March 2010
  • PublisherJohn Wiley & Sons Inc (US)
  • ISBN9780470454633

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.

Generalized Linear Models

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  • Author(s) Raymond H. Myers / Douglas C. Montgomery / G. Geoffrey Vining / Timothy J. Robinson
  • Subtitlewith Applications in Engineering and the Sciences
  • Edition1
  • Published4th March 2010
  • PublisherJohn Wiley & Sons Inc (US)
  • ISBN9780470454633

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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