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Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition (Chapman & Hall/CRC Texts in Statistical
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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.
New to the Second Edition
- Reorganized to focus on unbalanced data
- Reworked balanced analyses using methods for unbalanced data
- Introductions to nonparametric and lasso regression
- Introductions to general additive and generalized additive models
- Examination of homologous factors
- Unbalanced split plot analyses
- Extensions to generalized linear models
- R, Minitab®, and SAS code on the author’s website
The text can be used in a variety of courses, including a yearlong graduate course on regression and ANOVA or a data analysis course for upper-division statistics students and graduate students from other fields. It places a strong emphasis on interpreting the range of computer output encountered when dealing with unbalanced data.
- Sales Rank: #1602347 in Books
- Published on: 2015-12-22
- Original language: English
- Number of items: 1
- Dimensions: 10.25" h x 7.25" w x 2.00" l, .0 pounds
- Binding: Hardcover
- 636 pages
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
About the Author
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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