An Introduction to Generalized Linear Models 4th edition (fourth edition)

About this book

An Introduction to Generalized Linear Models 4th edition by Annette J. Dobson and Adrian G. Barnett gives students and researchers a clear framework for understanding generalized linear models and applying them to real statistical problems. The book develops the theory behind GLMs while showing how regression based methods can be used with continuous, binary, count, categorical, survival, and longitudinal data.

The 4th edition covers model fitting, exponential family distributions, estimation, statistical inference, normal linear models, multiple regression, logistic regression, nominal and ordinal models, Poisson regression, log linear models, survival analysis, clustered and longitudinal data, Bayesian analysis, and Markov chain Monte Carlo (MCMC) methods. It also includes practical examples and software applications using R, Stata, and WinBUGS.

This book is especially useful for Statistics Students, Biostatistics Students, Data Science Students, Epidemiology and Public Health Students, quantitative researchers, and graduate students who need a stronger understanding of regression and statistical modeling. It can also support researchers in medicine, engineering, business, and the social sciences who work with data that extend beyond ordinary linear regression.

If you are studying generalized linear models or need a practical reference for statistical analysis, this book gives you a structured way to understand model selection, estimation, inference, and interpretation across many types of data. The 4th Edition, 2018 is available as a searchable PDF enhanced digital version with instant access, making it easy to start using for coursework, research, data analysis, or professional reference right away.