By Alan Agresti
A worthy review of crucial principles and ends up in statistical modeling
Written via a highly-experienced writer, Foundations of Linear and Generalized Linear Models is a transparent and entire consultant to the most important recommendations and result of linear statistical versions. The e-book provides a vast, in-depth evaluate of the main typical statistical versions by way of discussing the speculation underlying the versions, R software program functions, and examples with crafted types to explain key rules and advertise functional version building.
The publication starts off by means of illustrating the basics of linear types, corresponding to how the model-fitting tasks the information onto a version vector subspace and the way orthogonal decompositions of the knowledge yield information regarding the results of explanatory variables. as a result, the ebook covers the most well-liked generalized linear types, which come with binomial and multi-nomial logistic regression for express facts, and Poisson and damaging binomial log linear versions for count number info.
Focusing at the theoretical underpinnings of those versions, Foundations of Linear and Generalized Linear Models additionally good points:
• An creation to quasi-likelihood equipment that require weaker distributional assumptions, resembling generalized estimating equation methods
• an summary of linear combined versions and generalized linear combined types with random results for clustered correlated facts, Bayesian modeling, and extensions to deal with complex instances corresponding to excessive dimensional difficulties
• a variety of examples that use R software program for all textual content facts analyses
• greater than four hundred workouts for readers to perform and expand the speculation, equipment, and knowledge research
• A supplementary site with datasets for the examples and routines a useful textbook for upper-undergraduate and graduate-level scholars in data and biostatistics classes, Foundations of Linear and Generalized Linear Models can also be a superb reference for practising statisticians and biostatisticians, in addition to somebody who's attracted to studying concerning the most crucial statistical types for interpreting information.