In previous chapters, we have seen how to model a binomial or Poisson response.

Multinomial response models can often be recast as Poisson responses and the stan-

dard linear model with a normal (Gaussian) response is already familiar. Although

these models each have their distinctive characteristics, we observe some common

features in all of them that we can abstract to form the generalized linear model

(GLM). By developing a theory and constructing general methods for GLMs, we are

able to tackle a wider range of data with different types of response variables. GLMs

were introduced by Nelder and Wedderburn (1972) while McCullagh and Nelder

(1989) provides a book-length treatment.