In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

You construct a generalized linear model by deciding on response and explanatory variables for your data and choosing an appropriate link function and response probability distribution. Some examples ...

In generalized linear models, the response is assumed to possess a probability distribution of the exponential form. That is, the probability density of the response Y for continuous response ...

Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the ... and nonlinear relationships between ...

Multiple regression and regression diagnostics. Generalised linear models; the exponential family, the linear predictor, link functions, analysis of deviance, parameter estimation, deviance residuals.

Regression models with intractable normalizing constants are valuable tools for analyzing complex data structures, yet ...

Wedderburn (1974) proposed a linear predictor and a link function to handle discrete ... a special area of statistics called the generalized linear model (GLM) (McCullagh and Nelder, 1989).