Generalized linear models (GLMs) are a class of statistical models that are an extension of linear regression models. They are used to analyze data where the response variable is not normally distributed or when the relationship between the response and predictor variables is not linear. GLMs allow for the specification of the distribution of the response variable as well as the use of non-linear link functions to relate the predictor variables to the response. This flexibility makes GLMs suitable for a wide range of data types, including binary, count, and categorical data. GLMs are widely used in many fields, including biostatistics, social sciences, and environmental sciences, for modeling and analyzing data with different types of response variables. They can also be used for hypothesis testing, prediction, and assessing the impact of predictor variables on the response variable.