By Alan Agresti
A important review of crucial rules and leads to statistical modeling
Written via a highly-experienced author, Foundations of Linear and Generalized Linear Models is a transparent and entire advisor to the major recommendations and result of linearstatistical types. The booklet provides a huge, in-depth evaluation of the main regularly usedstatistical versions by way of discussing the idea underlying the versions, R software program applications,and examples with crafted versions to explain key rules and advertise sensible modelbuilding.
The e-book starts by means of illustrating the basics of linear versions, corresponding to how the model-fitting initiatives the knowledge onto a version vector subspace and the way orthogonal decompositions of the knowledge yield information regarding the consequences of explanatory variables. therefore, the booklet covers the preferred generalized linear types, which come with binomial and multinomial logistic regression for specific information, and Poisson and unfavorable binomial loglinear versions for count number info. targeting the theoretical underpinnings of those models, Foundations ofLinear and Generalized Linear Models also features:
- An advent to quasi-likelihood equipment that require weaker distributional assumptions, corresponding to generalized estimating equation methods
- An review of linear combined versions and generalized linear combined types with random results for clustered correlated facts, Bayesian modeling, and extensions to address difficult situations corresponding to excessive dimensional problems
- Numerous examples that use R software program for all textual content facts analyses
- More than four hundred routines for readers to perform and expand the idea, tools, and information analysis
- A supplementary web site with datasets for the examples and exercises
a useful textbook for upper-undergraduate and graduate-level scholars in information and biostatistics courses, Foundations of Linear and Generalized Linear Models is additionally an outstanding reference for working towards statisticians and biostatisticians, in addition to somebody who's attracted to studying concerning the most vital statistical types for examining data.
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