GLM is an acronym that stands for Generalized Linear Model. It refers to a statistical model used to analyze and describe the relationship between a response variable and one or more explanatory variables. The Generalized Linear Model is an extension of the linear regression model and is applicable when the assumptions of the linear model are violated or when the response variable does not follow a normal distribution.
In GLM, the response variable can follow any probability distribution from the exponential family, including the normal, binomial, Poisson, gamma, and many others. This allows GLM to handle various types of data, such as continuous, binary, count, and non-negative continuous variables.
The GLM framework incorporates three main components: a linear predictor, a link function, and a probability distribution. The linear predictor specifies the relationship between the explanatory variables and the response variable, while the link function transforms the linear predictor into the domain of the response variable. The probability distribution, identified by the modeler, represents the uncertainty associated with the response variable.
GLM is widely used in fields such as econometrics, biostatistics, and social sciences to analyze data and make predictions. It provides a flexible and powerful tool to understand the relationships between variables, assess the impact of covariates on the response, and make predictions based on the model.
