Since the mathematics behind generalized linear models is often difficult to follow while the mathematics behind general linear models is well understood, this text describes the methodology behind both models in a parallel setup. After introducing a likelihood framework that is sufficient to cover both approaches, the authors present general linear models, including analysis of covariance, before moving on to more complicated generalized linear models using the same likelihood-based approach. Numerous simulated and real-world examples, implemented using R and SAS, illustrate the methods discussed. The text also provides exercises to further develop understanding.