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Linear Models by Shayle R. Searle, Marvin H. J. Gruber (Hardcover, 2016)

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By Shayle R. Searle, Marvin H.J. Gruber. 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.

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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.

Product Identifiers

Publisher
John Wiley & Sons Inc
ISBN-13
9781118952832
eBay Product ID (ePID)
223646734

Product Key Features

Author
Shayle R. Searle, Marvin H. J. Gruber
Publication Name
Linear Models
Format
Hardcover
Language
English
Subject
Mathematics
Publication Year
2016
Type
Textbook
Number of Pages
696 Pages

Dimensions

Item Height
235mm
Item Width
166mm
Item Weight
1110g

Additional Product Features

Title_Author
Marvin H. J. Gruber, Shayle R. Searle
Series Title
Wiley Series in Probability and Statistics
Country/Region of Manufacture
United States

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Linear Models by Shayle R. Searle, Marvin H. J. Gruber (Hardcover, 2016)

About this product

Product Information

Product Identifiers

Product Key Features

Dimensions

Additional Product Features

Best Selling in Adult Learning & University

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