Foundations of linear and generalized linear models agresti

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foundations of linear and generalized linear models agresti

Foundations of Linear and Generalized Linear Models by Alan Agresti

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 ofLinear 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.


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Statistics with R (3) - Generalized, linear, and generalized least squares models (LM, GLM, GLS)

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Alan Agresti

Foundations of Linear and Generalized Linear Models: An interview with author Alan Agresti

The Basic Library List Committee suggests that undergraduate mathematics libraries consider this book for acquisition. We will get to why that is not surprising after discussing what GLMs actually are. Most readers will be familiar with simple linear regression as taught in an introductory statistics class. That simple model is ambiguously named. That distinction is probably easiest to see in the common application in high school algebra of simultaneous equation solving to the problem of finding a parabola through three given points.

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You are currently using the site but have requested a page in the site. Would you like to change to the site? Alan Agresti. 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.

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.
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This month, Wiley is proud to publish the latest book by best-selling author, Professor Alan Agresti. Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linear statistical models. -

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3 thoughts on “Foundations of Linear and Generalized Linear Models by Alan Agresti

  1. The book begins by illustrating the fundamentals of linear models, such as how the Focusing on the theoretical underpinnings of these models,Foundations ofLinear and Generalized Linear Modelsalso features: An Alan Agresti.

  2. Foundations of Linear and Generalized Linear Models | Mathematical Association of America

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