This innovative, intermediate-level statistics text fills an
important gap by presenting the theory of linear statistical models
at a level appropriate for senior undergraduate or first-year
graduate students. With an innovative approach, the author's
introduces students to the mathematical and statistical concepts and
tools that form a foundation for studying the theory and applications
of both univariate and multivariate linear modelsA First Course in
Linear Model Theory systematically presents the basic theory behind
linear statistical models with motivation from an algebraic as well
as a geometric perspective. Through the concepts and tools of matrix
and linear algebra and distribution theory, it provides a framework
for understanding classical and contemporary linear model theory. It
does not merely introduce formulas, but develops in students the art
of statistical thinking and inspires learning at an intuitive level
by emphasizing conceptual understanding.The authors' fresh approach,
methodical presentation, wealth of examples, and introduction to
topics beyond the classical theory set this book apart from other
texts on linear models. It forms a refreshing and invaluable first
step in students' study of advanced linear models, generalized linear
models, nonlinear models, and dynamic models.
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