Linear Models in Statistics / Edition 2

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This unique book takes a look at linear models from a matrix perspective. With an emphasis on the theory of regression and analysis of variance, well-known author, Alvin Rencher, carefully develops the theory, lavishly illustrating it with numerically applied examples. He presents the material at an accessible level while incorporating motivational topics not usually present in books of this type.
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Editorial Reviews

SciTech Book News
Rencher...offers a textbook for a one-semester advanced undergraduate or beginning graduate course.... He includes more material than can actually squeeze into one semester...a good idea in statistics.
An excellent book. Highly recommended. Upper-division undergraduate and graduate students; professionals.
I would recommend the book to anyone as a reference book for the topics covered.... The book should also be a strong candidate for any M.S. course in linear models because of the numerous exercises with solutions and clear writing style.
Rencher (statistics, Brigham Young U.) offers a textbook for a one- semester advanced undergraduate or beginning graduate course for students who are conversant in calculus and matrix algebra and in statistics theory to the level of distribution of random variables, expected values, moment-generating functions, and elementary estimation and testing hypotheses. He includes more material than can actually squeeze into one semester to give the instructor some wiggle room<-->always a good idea in statistics. His goal is to prepare students to understand applications better and to read more deeply on such topics as mixed models and generalized linear models. Annotation c. Book News, Inc., Portland, OR (
From the Publisher
"This indeed clearly written book will do great service for advanced undergraduate and also for PhD students." (International Statistical Review, December 2008)

"This indeed clearly written book will do great service for advanced undergraduate and also for PhD students." (International Statistical Review, Dec 2008)

"This well-written book represents various topics on linear models with great clarity in an easy-to-understand style." (CHOICE, Aug 2008)

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Product Details

  • ISBN-13: 9780471754985
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 1/9/2008
  • Edition description: New Edition
  • Edition number: 2
  • Pages: 688
  • Sales rank: 1,026,271
  • Product dimensions: 6.42 (w) x 9.19 (h) x 1.59 (d)

Meet the Author

Alvin C. Rencher, PhD, is Professor of Statistics at Brigham Young University. Dr. Rencher is a Fellow of the American Statistical Association and the author of Methods of Multivariate Analysis and Multivariate Statistical Inference and Applications, both published by Wiley.

G. Bruce Schaalje, PhD, is Professor of Statistics at Brigham Young University. He has authored over 120 journal articles in his areas of research interest, which include mixed linear models, small sample inference, and design of experiments.

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Table of Contents

Matrix Algebra.
Random Vectors and Matrices.
Multivariate Normal Distribution.
Distribution of Quadratic Forms in y.
Simple Linear Regression.
Multiple Regression: Estimation.
Multiple Regression: Tests of Hypotheses and Confidence Intervals.
Multiple Regression: Model Validation and Diagnostics.
Multiple Regression: Random x's.
Analysis of Variance Models.
One-Way Analysis of Variance: Balanced Case.
Two-Way Analysis of Variance: Balanced Case.
Analysis of Variance: Unbalanced Data.
Analysis of Covariance.
Random Effects Models and Mixed Effects Models.
Additional Models.
Answers and Hints to Selected Problems.
Data Sets and SAS Files.
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