Foundations of Linear and Generalized Linear Models – eBook

Description

Discover the intricate world of statistical modeling with the essential resource, **Foundations of Linear and Generalized Linear Models (PDF)**. Crafted by a distinguished expert in the field, this comprehensive guide serves as an essential overview of the paramount ideas and findings in linear statistical models.

Delving deeply into the foundational aspects of statistical theory, this ebook provides an extensive examination of the most frequently utilized statistical models. Through its detailed discussions, readers will appreciate the theory that underpins these models, explore practical applications using R software, and engage with tailored examples that clarify critical concepts while promoting effective model development.

The exploration begins with a thorough introduction to linear models, elucidating how model fitting processes transform data into model vector subspaces. You will learn how orthogonal decompositions reveal insights into the influence of explanatory variables on your data sets. Building upon this groundwork, the text transitions into the realm of generalized linear models, highlighting important methodologies such as binomial and multinomial logistic regression for categorical outcomes, as well as Poisson and negative binomial loglinear models tailored for count data.

**Foundations of Linear and Generalized Linear Models** emphasizes the theoretical foundations of statistical modeling and includes:

• A wealth of practical examples utilizing R software for comprehensive data analyses
• An accompanying website featuring datasets specifically designed for the examples and exercises presented in the book
• Over 400 exercises crafted to reinforce concepts, expand upon theoretical understanding, and enhance data analysis skills
• An introduction to quasi-likelihood methods requiring less stringent distributional assumptions, including generalized estimating equations
• A comprehensive overview of linear mixed models and generalized linear mixed models incorporating random effects for clustered correlated data, as well as Bayesian modeling techniques and advanced strategies to address complex issues like high-dimensional data challenges

Positioned as an indispensable textbook for upper-undergraduate and graduate students pursuing degrees in statistics and biostatistics, **Foundations of Linear and Generalized Linear Models** also stands as a valuable reference for practicing statisticians and biostatisticians, as well as anyone eager to acquire knowledge on critical statistical models for effectively analyzing data.

**NOTE: This product includes the ebook, Foundations of Linear and Generalized Linear Models in PDF format. Please note that no access codes are provided.**

ISBN 978-1-56619-392-8 | ISBN 978-1-59325-240-7