ABSTRACT

Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those fo

chapter 1|24 pages

Introduction

chapter 2|30 pages

Binomial Data

chapter 3|14 pages

Count Regression

chapter 4|26 pages

Contingency Tables

chapter 5|18 pages

Multinomial Data

chapter 6|20 pages

Generalized Linear Models

chapter 7|18 pages

Other GLMs

chapter 8|32 pages

Random Effects

chapter 9|16 pages

Repeated Measures and Longitudinal Data

chapter 10|10 pages

Mixed Effect Models for Nonnormal Responses

chapter 11|20 pages

Nonparametric Regression

chapter 12|22 pages

Additive Models

chapter 13|16 pages

Trees

chapter 14|10 pages

Neural Networks