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
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Produktdetaljer
ISBN
9781441901187
Publisert
2019
Utgiver
Vendor
Springer
Språk
Product language
Engelsk
Format
Product format
Digital bok
Forfatter