Ronald Christensen is a Distinguished Professor of Statistics at the University of New Mexico.He is well known for his work on the theory and application of statistical models having linear structure.In addition to numerous technical articles, he is the author of  Plane Answers to Complex Questions:  The Theory of Linear Models; Advanced Linear Modeling:  Statistical Learning and Dependent Data; Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data and coauthor of Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians.Dr. Christensen is a fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics.  His is a past editor of The American Statistician and a past chair of the ASA's Section on Bayesian Statistical Science.

• Two-Dimensional Tables and Simple Logistic Regression.- Three-Dimensional Tables.- Logistic Regression, Logit Models, and Logistic Discrimination.- Independence Relationships and Graphical Models.- Model Selection Methods and Model Evaluation.- Models for Factors with Quantitative Levels.- Fixed and Random Zeros.- Generalized Linear Models.- The Matrix Approach to Log-Linear Models.- The Matrix Approach to Logit Models.- Maximum Likelihood Theory for Log-Linear Models.- Bayesian Binomial Regression. Exact Conditional Tests. - Correspondence Analysis.

Ronald Christensen is a Distinguished Professor of Statistics at the University of New Mexico.He is well known for his work on the theory and application of statistical models having linear structure.In addition to numerous technical articles, he is the author of  Plane Answers to Complex Questions:  The Theory of Linear Models; Advanced Linear Modeling:  Statistical Learning and Dependent Data; Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data and coauthor of Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians.Dr. Christensen is a fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics.  His is a past editor of The American Statistician and a past chair of the ASA's Section on Bayesian Statistical Science.

• Two-Dimensional Tables and Simple Logistic Regression.- Three-Dimensional Tables.- Logistic Regression, Logit Models, and Logistic Discrimination.- Independence Relationships and Graphical Models.- Model Selection Methods and Model Evaluation.- Models for Factors with Quantitative Levels.- Fixed and Random Zeros.- Generalized Linear Models.- The Matrix Approach to Log-Linear Models.- The Matrix Approach to Logit Models.- Maximum Likelihood Theory for Log-Linear Models.- Bayesian Binomial Regression. Exact Conditional Tests. - Correspondence Analysis.

Ronald Christensen is a Distinguished Professor of Statistics at the University of New Mexico.He is well known for his work on the theory and application of statistical models having linear structure.In addition to numerous technical articles, he is the author of  Plane Answers to Complex Questions:  The Theory of Linear Models; Advanced Linear Modeling:  Statistical Learning and Dependent Data; Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data and coauthor of Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians.Dr. Christensen is a fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics.  His is a past editor of The American Statistician and a past chair of the ASA's Section on Bayesian Statistical Science.

• Two-Dimensional Tables and Simple Logistic Regression.- Three-Dimensional Tables.- Logistic Regression, Logit Models, and Logistic Discrimination.- Independence Relationships and Graphical Models.- Model Selection Methods and Model Evaluation.- Models for Factors with Quantitative Levels.- Fixed and Random Zeros.- Generalized Linear Models.- The Matrix Approach to Log-Linear Models.- The Matrix Approach to Logit Models.- Maximum Likelihood Theory for Log-Linear Models.- Bayesian Binomial Regression. Exact Conditional Tests. - Correspondence Analysis.