Providing a wide-ranging introduction to the use of linear models in analyzing data, this text presents a vector space and projections approach to the subject. The topics covered include ANOVA, estimation, hypothesis testing, multiple comparison, regression analysis, and experimental design.
Linear Modeling for Unbalanced Data, Second Edition
The second edition delves into modeling unbalanced data, introducing new chapters on logistic regression, log-linear models, and time-to-event data. It emphasizes modeling main effects and interactions while incorporating advanced techniques such as nonparametric, lasso, and generalized additive regression models. The text also offers a thorough analysis of small unbalanced datasets, making it a comprehensive resource for understanding complex statistical modeling.
The third edition of this companion volume to Ronald Christensen's work expands on linear modeling concepts to cover Statistical Learning and Dependent Data. It includes new content on nonparametric regression, penalized estimation, and various linear models. R code for analyses is available online, making it a comprehensive resource.
The second edition of Log-Linear Models emphasizes logistic regression, with a restructured format. It covers fundamental to advanced topics, including Bayesian biomial regression in Chapter 13. The text is refined, with consistent numbering for examples and equations, enhancing clarity for readers.
The book explores topics such as logistic discrimination and generalised linear models, and builds upon the relationships between these basic models for continuous data and the analogous log-linear and logistic regression models for discrete data.