Generalized Linear Models (GLM)

Generalized Linear Models (GLM) are a flexible framework used in statistics to model relationships between a response variable and one or more predictors. Unlike traditional linear models, GLMs can handle various types of data, such as binary outcomes (yes/no) or counts (number of events). They consist of three key components: a random component (describing the response's distribution), a systematic component (the predictors), and a link function (connecting the two). This allows GLMs to generalize linear regression, making them suitable for a wider range of applications, including health, finance, and social sciences.