- Author: Alan Agresti
- File Size: 5 MB
- Format: PDF
- Length: 480 pages
- Publisher: Wiley
- Publication Date: January 15, 2015
- Language: English
- ASIN: B013RKYR6C
- ISBN-10: 1118730038
- ISBN-13: 9781118730034
A valuable overview of the most important ideas and results in statistical modeling
Written by a highly-seasoned author, Foundations of Linear and Generalized Linear Models (PDF) is a clear and comprehensive guide to the key concepts and results of linear statistical models. The ebook presents a broad, in-depth overview of the most commonly used statistical models by discussing the theory implicit in the models, R software applications, and examples with crafted models to elucidate main ideas and encourage practical model building.
The ebook starts by illustrating the basics 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 ebook covers the most popular generalized linear models, which encompass binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Emphasizing on the theoretical underpinnings of these models, Foundations of Linear and Generalized Linear Models also features:
An indispensable 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.
NOTE: The product includes the ebook, Foundations of Linear and Generalized Linear Models in PDF. No access codes are included.
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