Provides an easy-to-understand guide to statistical linear models
and its uses in data analysis This book defines a broad spectrum of
statistical linear models that is useful in the analysis of data.
Considerable rewriting was done to make the book more reader
friendly than the first edition. Linear Models, Second Edition is
written in such a way as to be self-contained for a person with a
background in basic statistics, calculus and linear algebra. The
text includes numerous applied illustrations, numerical examples,
and exercises, now augmented with computer outputs in SAS and R.
Also new to this edition is: A greatly improved internal design and
format A short introductory chapter to ease understanding of the
order in which topics are taken up Discussion of additional topics
including multiple comparisons and shrinkage estimators Enhanced
discussions of generalized inverses, the MINQUE, Bayes and Maximum
Likelihood estimators for estimating variance components
Furthermore, in this edition, the second author adds many
pedagogical elements throughout the book. These include numbered
examples, end-of-example and end-of-proof symbols, selected hints
and solutions to exercises available on the book s website, and
references to big data in everyday life. Featuring a thorough
update, Linear Models, Second Edition includes: A new internal
format, additional instructional pedagogy, selected hints and
solutions to exercises, and several more real-life applications
Many examples using SAS and R with timely data sets Over 400
examples and exercises throughout the book to reinforce
understanding Linear Models, Second Edition is a textbook and a
reference for upper-level undergraduate and beginning
graduate-level courses on linear models, statisticians, engineers,
and scientists who use multiple regression or analysis of variance
in their work. SHAYLE R. SEARLE, PhD, was Professor Emeritus of
Biometry at Cornell University. He was the author of the first
edition of Linear Models, Linear Models for Unbalanced Data, and
Generalized, Linear, and Mixed Models (with Charles E. McCulloch),
all from Wiley. The first edition of Linear Models appears in the
Wiley Classics Library. MARVIN H. J. GRUBER, PhD, is Professor
Emeritus at Rochester Institute of Technology, School of
Mathematical Sciences. Dr. Gruber has written a number of papers
and has given numerous presentations at professional meetings
during his tenure as a professor at RIT. His fields of interest
include regression estimators and the improvement of their
efficiency using shrinkage estimators. He has written and published
two books on this topic. Another of his books, Matrix Algebra for
Linear Models, also published by Wiley, provides good preparation
for studying Linear Models. He is a member of the American
Mathematical Society, the Institute of Mathematical Statistics and
the American Statistical Association.
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