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Generalized linear models generalize the relations between predictor variables and the mean of a distribution. In normal linear regression, the mean is equal to a linear combination of predictors. However, in generalized linear models, a function of the mean is equal to the linear predictor. The need for this function arises to preserve the linearity structure and adjust for non-linear relationships in the data.