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An Introduction to Generalized Linear Models, Third Edition (Chapman & Hall/CRC Texts in Statistical Science)

An Introduction to Generalized Linear Models, Third Edition (Chapman & Hall/CRC Texts in Statistical Science)

An Introduction to Generalized Linear Models, Third Edition (Chapman & Hall/CRC Texts in Statistical Science) Paperback - 2008

by Barnett, Adrian G

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  • Title An Introduction to Generalized Linear Models, Third Edition (Chapman & Hall/CRC Texts in Statistical Science)
  • Author Barnett, Adrian G
  • Binding Paperback
  • Edition INTERNATIONAL ED
  • Condition Used - Very good
  • Pages 307
  • Volumes 1
  • Language ENG
  • Publisher CRC Press
  • Publication date 2008-05
  • Illustrated Yes
  • Bookseller's Inventory # GOR004441844
  • ISBN 9781584889502 / 1584889500
  • Weight 0.99 lbs (0.45 kg)
  • Dimensions 9.18 x 6.1 x 0.65 in (23.32 x 15.49 x 1.65 cm)
  • Category Mathematics
  • Library of Congress subjects Linear models (Statistics)
  • Library of Congress Catalogue Number 2008013034
  • Dewey Decimal Code 519.5
  • Quantity available 1

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Reader reviews for An Introduction to Generalized Linear Models, Third Edition (Chapman & Hall/CRC Texts in Statistical Science)

From the publisher

Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis.

Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods.

Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons. It includes examples and exercises with complete data sets for nearly all the models covered.

First line

This book is designed to introduce the reader to generalized linear models; these provide a unifying framework for many commonly used statistical techniques.
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