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Introduction To Generalized Linear Models (Paperback, 4th edition)
Loot Price: R489
Discovery Miles 4 890
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Introduction To Generalized Linear Models (Paperback, 4th edition)
Series: Chapman & Hall/CRC Texts in Statistical Science
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Loot Price R489
Discovery Miles 4 890
Expected to ship within 15 working days
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An Introduction to Generalized Linear Models, Fourth Edition
provides a cohesive framework for statistical modelling, with an
emphasis on numerical and graphical methods. This new edition of a
bestseller has been updated with new sections on non-linear
associations, strategies for model selection, and a Postface on
good statistical practice. 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. Introduces GLMs in a
way that enables readers to understand the unifying structure that
underpins them Discusses common concepts and principles of advanced
GLMs, including nominal and ordinal regression, survival analysis,
non-linear associations and longitudinal analysis Connects Bayesian
analysis and MCMC methods to fit GLMs Contains numerous examples
from business, medicine, engineering, and the social sciences
Provides the example code for R, Stata, and WinBUGS to encourage
implementation of the methods Offers the data sets and solutions to
the exercises online Describes the components of good statistical
practice to improve scientific validity and reproducibility of
results. Using popular statistical software programs, this concise
and accessible text illustrates practical approaches to estimation,
model fitting, and model comparisons.
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