https://www.dataspoof.info/post/generalized-linear-regression-in-python/ Generalized Linear Regression (GLR) is a statistical framework that extends the concept of linear regression to handle various types of response variables beyond the continuous numeric outcomes in traditional linear regression. GLR is particularly useful when dealing with non-normal and non-continuous data types, such as binary, count, and categorical data. In regular linear regression, the response variable (dependent variable) is assumed to be continuous and normally distributed, and the goal is to model the relationship between the response variable and one or more predictor variables (independent variables) through a linear equation. The equation can be represented as: � = � 0 + � 1 � 1 + � 2 � 2 + … + � � � � + � Y = β 0 + β 1 X 1 + β 2 X 2 + … + β n X n + ε Where: � Y is the response variable � 0 β 0 is the intercept � 1 , � 2 , … , � � β 1 , β 2 , … , β n are the coefficients corres...
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