Generalized linear models (GLMs) are a flexible generalization of ordinary linear regression that allows for the response variable to have a distribution other than a normal distribution. They connect the linear predictor, a linear combination of unknown parameters, to a response variable through a link function, making them suitable for various types of data, including binary outcomes, count data, and proportions.
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