Poisson regression is also a special case of the generalized linear model, where the random component is specified by the Poisson distribution. This usually works well when the response variable is a count of some occurrence, such as the number of calls to a customer service number in an hour or the number of cars that pass through an intersection in a day. Unlike the binomial distribution, which counts the number of successes in a given number of trials, a Poisson count is not bounded above.

When all explanatory variables are discrete, the Poisson regression model is equivalent to the log-linear model, which we will see in the next lesson. For the present discussion, however, we'll focus on model-building and interpretation. We'll see that many of these techniques are very similar to those in the logistic regression model.