Introduction Background Scope Notation Distributions Related to the Normal Distribution Quadratic Forms Estimation Model Fitting Introduction Examples Some Principles of Statistical Modeling Notation and Coding for Explanatory Variables Exponential Family and Generalized Linear Models Introduction Exponential Family of Distributions Properties of Distributions in the Exponential Family Generalized Linear Models Examples Estimation Introduction Example: Failure Times for Pressure Vessels Maximum Likelihood Estimation Poisson Regression Example Inference Introduction Sampling Distribution for Score Statistics Taylor Series Approximations Sampling Distribution for MLEs Log-Likelihood Ratio Statistic Sampling Distribution for the Deviance Hypothesis Testing Normal Linear Models Introduction Basic Results Multiple Linear Regression Analysis of Variance Analysis of Covariance General Linear Models Binary Variables and Logistic Regression Probability Distributions Generalized Linear Models Dose Response Models General Logistic Regression Model Goodness-of-Fit Statistics Residuals Other Diagnostics Example: Senility and WAIS Nominal and Ordinal Logistic Regression Introduction Multinomial Distribution Nominal Logistic Regression Ordinal Logistic Regression General Comments Poisson Regression and Log-Linear Models Introduction Poisson Regression Examples of Contingency Tables Probability Models for Contingency Tables Log-Linear Models Inference for Log-Linear Models Numerical Examples Remarks Survival Analysis Introduction Survivor Functions and Hazard Functions Empirical Survivor Function Estimation Inference Model Checking Example: Remission Times Clustered and Longitudinal Data Introduction Example: Recovery from Stroke Repeated Measures Models for Normal Data Repeated Measures Models for Non-Normal Data

Popular for its accessible, concise, and clear introduction to this key statistical methodology, An Introduction to Generalized Linear Models, Third Edition provides a wealth of examples from such diverse fields as business, medicine, engineering, and the social sciences. Emphasizing graphical methods for exploratory data analysis and visualization, this new edition offers more material on Bayesian methodology and additional advice on implementing methods using statistical software. It also has updated the examples and exercises and includes an appendix of selected solutions, enhancing its suitability for self-study.