<human>:  Can you explain how Poisson regression works?
<bot>: Poisson regression is a type of generalized linear model used to predict count data: numbers of events over a period of time, such as the number of cars passing by a certain point on a road in an hour. It allows researchers to model the relationship between an outcome variable that is a count and one or more predictor variables. It works by taking the outcome variable and modeling it as a function of the predictor variables, while accounting for the distribution of the count data. In this way, it allows us to estimate the relative strength of the predictor variables. The model is then fitted using a maximum likelihood estimation.