Linear Models in Statistics (PDF)

The essential introduction to the theory and application of linear
models--now in a valuable new edition

Since most advanced statistical tools are generalizations of the
linear model, it is neces-sary to first master the linear model in
order to move forward to more advanced concepts. The linear model
remains the main tool of the applied statistician and is central to
the training of any statistician regardless of whether the focus is
applied or theoretical. This completely revised and updated new
edition successfully develops the basic theory of linear models for
regression, analysis of variance, analysis of covariance, and
linear mixed models. Recent advances in the methodology related to
linear mixed models, generalized linear models, and the Bayesian
linear model are also addressed.

Linear Models in Statistics, Second Edition includes full
coverage of advanced topics, such as mixed and generalized linear
models, Bayesian linear models, two-way models with empty cells,
geometry of least squares, vector-matrix calculus, simultaneous
inference, and logistic and nonlinear regression. Algebraic,
geometrical, frequentist, and Bayesian approaches to both the
inference of linear models and the analysis of variance are also
illustrated. Through the expansion of relevant material and the
inclusion of the latest technological developments in the field,
this book provides readers with the theoretical foundation to
correctly interpret computer software output as well as effectively
use, customize, and understand linear models.

This modern Second Edition features:

* New chapters on Bayesian linear models as well as random and
mixed linear models

* Expanded discussion of two-way models with empty cells

* Additional sections on the geometry of least squares

* Updated coverage of simultaneous inference

The book is complemented with easy-to-read proofs, real data
sets, and an extensive bibliography. A thorough review of the
requisite matrix algebra has been addedfor transitional purposes,
and numerous theoretical and applied problems have been
incorporated with selected answers provided at the end of the book.
A related Web site includes additional data sets and SAS® code
for all numerical examples.

Linear Model in Statistics, Second Edition is a must-have book
for courses in statistics, biostatistics, and mathematics at the
upper-undergraduate and graduate levels. It is also an invaluable
reference for researchers who need to gain a better understanding
of regression and analysis of variance.