
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
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 |
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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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