Another very popular generalized linear model is the Poisson regression. This model assumes data is distributed according to the, wait for it... Poisson distribution.

One scenario where Poisson distribution is useful is when counting things, such as the decay of a radioactive nucleus, the number of children per couple, or the number of Twitter followers. What all these examples have in common is that we usually model them using discrete non-negative numbers: {0, 1, 2, 3, ....}. This type of variable receives the name of count data.