Generalized linear models are a type of models that go beyond traditional linear models for analyzing data. They can handle a wider range of data patterns and distributions. These models were first introduced in 1972 and have been further developed since then. They include logistic regression and Poisson regression as special cases for specific types of data analysis. The models rely on specific types of distributions, like the exponential family and gamma distribution. The process involves estimating parameters, fitting the model, and testing the model's goodness of fit. Overall, generalized linear models offer a flexible and powerful tool for analyzing various types of data.