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Generalized Linear Models With Examples in R (Hardcover, 1st ed. 2018)
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Generalized Linear Models With Examples in R (Hardcover, 1st ed. 2018)
Series: Springer Texts in Statistics
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This textbook presents an introduction to generalized linear
models, complete with real-world data sets and practice problems,
making it applicable for both beginning and advanced students of
applied statistics. Generalized linear models (GLMs) are powerful
tools in applied statistics that extend the ideas of multiple
linear regression and analysis of variance to include response
variables that are not normally distributed. As such, GLMs can
model a wide variety of data types including counts, proportions,
and binary outcomes or positive quantities. The book is designed
with the student in mind, making it suitable for self-study or a
structured course. Beginning with an introduction to linear
regression, the book also devotes time to advanced topics not
typically included in introductory textbooks. It features chapter
introductions and summaries, clear examples, and many practice
problems, all carefully designed to balance theory and practice.
The text also provides a working knowledge of applied statistical
practice through the extensive use of R, which is integrated into
the text. Other features include: * Advanced topics such as power
variance functions, saddlepoint approximations, likelihood score
tests, modified profile likelihood, small-dispersion asymptotics,
and randomized quantile residuals * Nearly 100 data sets in the
companion R package GLMsData * Examples that are cross-referenced
to the companion data set, allowing readers to load the data and
follow the analysis in their own R session
General
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