You construct a generalized linear model by deciding on response and explanatory variables for your data and choosing an appropriate link function and response probability distribution. Some examples of generalized linear models follow. Explanatory variables can be any combination of continuous variables, classification variables, and interactions.
response variable: a continuous variable
distribution: normal
link function: identity,
response variable: a count
distribution: Poisson
link function: log,
response variable: a positive, continuous variable
distribution: gamma
link function: log,