Analysis of Variance, Design, and Regression, 2nd Edition, 9781498730143
Hardcover

Analysis of Variance, Design, and Regression, 2nd Edition

linear modeling for unbalanced data, second edition

$216.80

  • Hardcover

    636 pages

  • Release Date

    22 December 2015

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Summary

Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling data with an emphasis on how to incorporate specific ideas (hypotheses) about the structure of the data into a linear model for the data. The book carefully analyzes small data sets by using tools that are easily scaled to big data. The tools also apply to small relevant data sets that are extracted from big data.

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Book Details

ISBN-13:9781498730143
ISBN-10:1498730140
Series:Chapman & Hall/CRC Texts in Statistical Science
Author:Ronald Christensen
Publisher:Taylor & Francis Inc
Imprint:Chapman & Hall/CRC
Format:Hardcover
Number of Pages:636
Edition:2nd
Release Date:22 December 2015
Weight:1.29kg
Dimensions:254mm x 178mm
What They're Saying

Critics Review

Praise for the First Edition:“… written in a clear and lucid style … an excellent candidate for a beginning level graduate textbook on statistical methods … a useful reference for practitioners.”—Zentralblatt für Mathematik

Being devoted to students mainly, each chapter includes illustrative examples and exercises. The most important thing about this book is that it provides traditional tools for future approaches in the big data domain since, as the author says, the machine learning techniques are directly based on the fundamental statistical methods.” ~Marina Gorunescu (Craiova)

About The Author

Ronald Christensen

Ronald Christensen is a professor of statistics in the Department of Mathematics and Statistics at the University of New Mexico. Dr. Christensen is a fellow of the American Statistical Association (ASA) and Institute of Mathematical Statistics. He is a past editor of The American Statistician and a past chair of the ASA’s Section on Bayesian Statistical Science. His research interests include linear models, Bayesian inference, log-linear and logistic models, and statistical methods.

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