Starting with the basic linear model where the design and
covariance matrices are of full rank, this book demonstrates how
the same statistical ideas can be used to explore the more general
linear model with rank-deficient design and/or covariance matrices.
The unified treatment presented here provides a clearer
understanding of the general linear model from a statistical
perspective, thus avoiding the complex matrix-algebraic arguments
that are often used in the rank-deficient case. Elegant geometric
arguments are used as needed.The book has a very broad coverage,
from illustrative practical examples in Regression and Analysis of
Variance alongside their implementation using R, to providing
comprehensive theory of the general linear model with 181
worked-out examples, 227 exercises with solutions, 152 exercises
without solutions (so that they may be used as assignments in a
course), and 320 up-to-date references.This completely updated and
new edition of Linear Models: An Integrated Approach includes the
following features:
General
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