Why Use Generalised Linear Model?

Generalized linear models cover all these situations by allowing for response variables that have arbitrary distributions (rather than simply normal distributions), and for an arbitrary function of the response variable (the link function) to vary linearly with the predictors (rather than assuming that the response ...

Why do you use generalized linear model?

GLM models allow us to build a linear relationship between the response and predictors, even though their underlying relationship is not linear. ... Unlike Linear Regression models, the error distribution of the response variable need not be normally distributed.

What is the difference between OLS and glm?

In generalized linear models, though, ρ=Xβ, so that the relationship to E(Y)=μ=g−1(ρ). In OLS the assumption is that the residuals follow a normal distribution with mean zero, and constant variance. This is not the case in glm, where the variance in the predicted values to be a function of E(y).