Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, 2nd Edition, (PDF) offers linear structures for modeling data with a focus on how to include specific ideas (hypotheses) about the structure of the data into a linear model for the data. The ebook carefully evaluates small data sets by using tools that are easily scaled to large data. The tools also apply to small relevant data sets that are taken from big data.
New to the 2nd Edition
- Unbalanced split-plot analyses
- Examination of homologous factors
- Extensions to generalized linear models
- Reorganized to focus on unbalanced data
- Introductions to nonparametric and lasso regression
- R, Minitab®, and SAS code on the author’s website
- Introductions to general additive and generalized additive models
- Reworked balanced analyses using methods for unbalanced data
The text can be used in a number of courses, including ANOVA or a data analysis course for upper-division statistics and a yearlong graduate course on regression for students and graduate students from other fields. It places a strong focus on interpreting the range of computer output faced when dealing with unbalanced data.
Reviews
“… written in a lucid and clear style … an excellent option for a beginning level graduate textbook on statistical methods … a helpful reference for practitioners.” ― Zentralblatt für Mathematik
“Being dedicated to students mainly, each chapter has illustrative examples and exercises. The most significant thing about this ebook is that it offers traditional tools for future approaches in the big data domain since, as the author says, the machine learning techniques are straightaway based on the fundamental statistical methods.” ― Marina Gorunescu (Craiova)
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