James W. Hardin is a professor and the Biostatistics division head in the Department of Epidemiology and Biostatistics at the University of South Carolina. He is also the associate dean for Faculty Affairs and Curriculum of the Arnold School of Public Health at the University of South Carolina.Joseph M. Hilbe was a professor emeritus at the University of Hawaii and an adjunct professor of sociology and statistics at Arizona State University.

• Foundations of Generalized Linear Models. GLMs. GLM estimation algorithms. Analysis of fit. Continuous Response Models. The Gaussian family. The gamma family. The inverse Gaussian family. The power family and link. Binomial Response Models. The binomial–logit family. The general binomial family. The problem of overdispersion. Count Response Models. The Poisson family. The negative binomial family. Other count-data models. Multinomial Response Models. Unordered-response family. The ordered-response family. Extensions to the GLM. Extending the likelihood. Clustered data. Bivariate and multivariate models. Bayesian GLMs. Stata Software. Programs for Stata. Data synthesis.

James W. Hardin is a professor and the Biostatistics division head in the Department of Epidemiology and Biostatistics at the University of South Carolina. He is also the associate dean for Faculty Affairs and Curriculum of the Arnold School of Public Health at the University of South Carolina.Joseph M. Hilbe was a professor emeritus at the University of Hawaii and an adjunct professor of sociology and statistics at Arizona State University.

• Foundations of Generalized Linear Models. GLMs. GLM estimation algorithms. Analysis of fit. Continuous Response Models. The Gaussian family. The gamma family. The inverse Gaussian family. The power family and link. Binomial Response Models. The binomial–logit family. The general binomial family. The problem of overdispersion. Count Response Models. The Poisson family. The negative binomial family. Other count-data models. Multinomial Response Models. Unordered-response family. The ordered-response family. Extensions to the GLM. Extending the likelihood. Clustered data. Bivariate and multivariate models. Bayesian GLMs. Stata Software. Programs for Stata. Data synthesis.

James W. Hardin is a professor and the Biostatistics division head in the Department of Epidemiology and Biostatistics at the University of South Carolina. He is also the associate dean for Faculty Affairs and Curriculum of the Arnold School of Public Health at the University of South Carolina.Joseph M. Hilbe was a professor emeritus at the University of Hawaii and an adjunct professor of sociology and statistics at Arizona State University.

• Foundations of Generalized Linear Models. GLMs. GLM estimation algorithms. Analysis of fit. Continuous Response Models. The Gaussian family. The gamma family. The inverse Gaussian family. The power family and link. Binomial Response Models. The binomial–logit family. The general binomial family. The problem of overdispersion. Count Response Models. The Poisson family. The negative binomial family. Other count-data models. Multinomial Response Models. Unordered-response family. The ordered-response family. Extensions to the GLM. Extending the likelihood. Clustered data. Bivariate and multivariate models. Bayesian GLMs. Stata Software. Programs for Stata. Data synthesis.