We just saw how to fit our data to a model using linear regression. However, as we just saw, in order for our model to be valid, it must make the assumption that the variance is constant and the errors are normally distributed. A generalized linear model (GLM) is an alternative approach to linear regression, which allows the errors to follow probability distributions other than a normal distribution. GLM is typically used for response variables that represent count data or binary response variables. To fit your data to a GLM in R, you can use the
GLM has three important properties:
The error structure informs us of the error distribution to use to model ...