A Generalized Linear Model (GLM) is a flexible statistical framework that extends traditional linear regression to accommodate various types of response variables. Unlike standard linear models that assume a normal distribution of errors, GLMs can handle different distributions, such as binomial for binary outcomes or Poisson for count data. This is achieved through a link function that connects the linear predictor to the mean of the distribution. GLMs consist of three main components: a random component, a systematic component, and a link function. The random component specifies the probability distribution of the response variable, while the systematic component represents the linear combination of predictors. The link function transforms the expected value of the response variable to ensure it fits within the appropriate range, making GLMs versatile for various applications in statistics and data analysis.