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Foundations Of Linear And Generalized Linear Models (Cód: 9235224)

Agresti, Alan

John Wiley & Sons

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Descrição

A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, 'Foundations of Linear and Generalized Linear Models 'is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications, and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, 'Foundations of''Linear and Generalized Linear Models 'also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, 'Foundations of Linear and Generalized Linear Models 'is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data. A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, 'Foundations of Linear and Generalized Linear Models 'is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications, and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, 'Foundations of' 'Linear and Generalized Linear Models 'also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, 'Foundations of Linear and Generalized Linear Models 'is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data. Alan Agresti, PhD, is Distinguished Professor Emeritus in the Department of Statistics at the University of Florida. He has presented short courses on generalized linear models and categorical data methods in more than 30 countries. The author of over 200 journal articles, Dr. Agresti is also the author of 'Categorical Data Analysis,' Third Edition, 'Analysis' 'of Ordinal Categorical Data,' Second Edition, and 'An Introduction to Categorical Data' 'Analysis,' Second Edition, all published by Wiley.

Características

Peso 0.79 Kg
Produto sob encomenda Sim
Marca John Wiley & Sons
I.S.B.N. 9781118730034
Referência 032452480
Altura 24.30 cm
Largura 16.40 cm
Profundidade 2.90 cm
Número de Páginas 472
Idioma Inglês
Acabamento Capa dura
Cód. Barras 9781118730034
Ano da edição 2015
AutorAgresti, Alan