By Alan Agresti
ISBN-10: 1118730305
ISBN-13: 9781118730300
A worthy review of an important rules and ends up in statistical modeling
Written through a highly-experienced writer,
Foundations of Linear and Generalized Linear Models is a transparent and accomplished advisor to the main techniques and result of linear statistical versions. The ebook provides a wide, in-depth review of the main generic statistical versions by way of discussing the speculation underlying the types, R software program functions, and examples with crafted versions to explain key rules and advertise sensible version building.
The ebook starts via illustrating the basics of linear versions, resembling how the model-fitting initiatives the information onto a version vector subspace and the way orthogonal decompositions of the information yield information regarding the results of explanatory variables. as a result, the publication covers the preferred generalized linear versions, which come with binomial and multi-nomial logistic regression for specific information, and Poisson and unfavorable binomial log linear types for count number info.
Focusing at the theoretical underpinnings of those versions,
Foundations of Linear and Generalized Linear Models additionally positive factors:
• An advent to quasi-likelihood tools that require weaker distributional assumptions, similar to generalized estimating equation methods
• an outline of linear combined types and generalized linear combined types with random results for clustered correlated facts, Bayesian modeling, and extensions to address challenging circumstances akin to excessive dimensional difficulties
• quite a few examples that use R software program for all textual content information analyses
• greater than four hundred routines for readers to perform and expand the idea, tools, and knowledge research
• A supplementary web site with datasets for the examples and workouts a useful textbook for upper-undergraduate and graduate-level scholars in information and biostatistics classes,
Foundations of Linear and Generalized Linear Models can be a great reference for practising statisticians and biostatisticians, in addition to someone who's attracted to studying in regards to the most vital statistical types for studying info.