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This book examines statistical models for frequency data.  The primary focus is on log-linear models for contingency tables but also includes extensive discussion of logistic regression.  Topics such as logistic discrimination, generalized linear models, and correspondence analysis are also explored.

The treatment is designed for readers with prior knowledge of analysis of variance and regression.  It builds upon the relationships between these basic models for continuous data and the analogous log-linear and logistic regression models for discrete data.  While...

This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The author's emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models, based on projections, orthogonality, and other vector space ideas. Every chapter comes with numerous exercises and examples that make it ideal for a graduate-level course. All of the standard topics are covered in depth: ANOVA, estimation including Bayesian estimation, hypothesis testing, multiple comparisons, regression analysis, and experimental...

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...