Introduction Estimation Testing Hypotheses One-Way ANOVA Multiple Comparison Techniques Regression Analysis Multifactor Analysis of Variance Experimental Design Models Analysis of Covariance Estimation and Testing in General Gauss-Markov Models Split Plot Models Mixed Models and Variance Components Checking Assumptions, Residuals, and Influential Observations Variable Selection and Collinearity
Introduction * Estimation * Testing Hypotheses * One-Way ANOVA * Multiple Comparison Techniques * Regression Analysis * Multifactor Analysis of Variance * Experimental Design Models * Analysis of Covariance * Estimation and Testing in General Gauss-Markov Models * Split Plot Models * Mixed Models and Variance Components * Checking Assumptions, Residuals, and Influential Observations * Variable Selection and Collinearity
This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The authors 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. In addition, the book covers topics that are not usually treated at this level, but which are important in their own right. The author, Ronald Christensen, is a Professor of Statistics at the University of New Mexico.
This book contains several changes from past editions including a new section that introduces generalized linear models. It will be of interest to researchers and graduate students in pure and applied statistics.