An Introduction to Liner Models/ Annette J, Dobson & Adrian G. Barnett.

1. Introduction 2. Model fitting 3. Exponential family and generalized linear models 4. Estimation 5. Inference 6. Normal linear models 7. Binary variables and logistic regression 8.Nominal and ordinal logistic regression 9. Poisson regression and log-linear models 10. Survival analysis 11. Clustered and longitudinal data 12. Bayesian analysis 13. Markov chain mote Carlo methods 14. Example Bayesian analyses

Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modelling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis.
Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modelling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods.

Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons. It includes examples and exercises with complete data sets for nearly all the models covered.