Titlebook: Generalized Linear Models With Examples in R; Peter K. Dunn,Gordon K.‘Smyth Textbook 2018 Springer Science+Business Media, LLC, part of Sp

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Chapter 3: Linear Regression Models: Diagnostics and Model-Building,regression models are first reviewed (Sect. .), then residuals, the main tools of diagnostic analysis, are defined (Sect. .). We follow with a discussion of the leverage, a measure of the location of an observation relative to the average observation location (Sect. .). The various diagnostic tools

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Chapter 2: Linear Regression Models,guage used in this book so a common foundation is laid for all readers for the upcoming study of generalized linear models: linear regression models are a special case of generalized linear models. We first define linear regression models and introduce the relevant notation and assumptions. We then

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Chapter 7: Generalized Linear Models: Inference,re applied in the context of .s. We first consider inference when . is known (Sect. .), then the large-sample asymptotic results (Sect. .) that underlie all the distributional results for the test statistics in that section. Section . then introduces goodness-of-fit tests to determine whether the li