Part of a series on regression analysis Generalized linear models are a flexible class of models that let us generalize from the linear model to include more types of response variables, such as count, binary, and proportion data. The data Y1, Y2, ..., Yn are independently distributed, i.e., cases are independent. Thus errors are independent... but NOT necessarily normally distributed. The dependent variable Yi does NOT need to be normally distributed, but it assumes a distribution, typically from an exponential family (e.g. binomial, Poisson, gamma,...) GLM does NOT assume a linear relationship between the dependent variable and the independent variables, but it does assume a linear relationship between the transformed response (in terms of the link function) and the explanatory variables; e.g., for binary logistic regression logit(p)=β0+β1X. The homogeneity of variance does NOT need to be satisfied. It uses maximum likelihood estimation (MLE) rather than ordinary least squares (OLS) ...
