Poisson regression is a statistical technique used to model count data, which refers to data that represents the number of times an event occurs within a fixed interval of time or space. This method is particularly useful when the response variable is a count, such as the number of occurrences of an event, and is assumed to follow a Poisson distribution.

In a Poisson regression model, the event counts are modeled as a function of one or more predictor variables. The relationship between the predictors and the response variable is established through a link function, typically the exponential function, which ensures that the predicted counts remain non-negative. The model is trained using Maximum Likelihood Estimation (MLE) to find the best-fitting regression coefficients that maximize the likelihood of observing the given data ¹².

Poisson regression is widely applied in various fields, including epidemiology, traffic accident analysis, and any scenario where count data is prevalent. It allows researchers to understand the impact of different factors on the occurrence of events.