Generalized Linear Models
Generalized Linear Models (GLMs) are a class of statistical models that allow for the analysis of data with non-normal distributions, such as binary, count, or skewed data. GLMs extend the linear regression model by allowing the response variable to have a distribution other than the normal distribution. They consist of three components: a random component that specifies the distribution of the response variable, a linear predictor that combines the predictor variables linearly, and a link function that relates the linear predictor to the expected value of the response variable. GLMs are widely used in various fields, including finance, biology, and social sciences, to model relationships between variables and make predictions based on the data.
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