Poisson regression is a statistical technique used to model count data, which refers to the number of times an event occurs within a fixed period or space. It assumes that the counts follow a Poisson distribution, where the mean and variance are equal. This method is particularly useful when the data consists of non-negative integers, such as the number of customer arrivals at a store or the number of accidents at an intersection. In Poisson regression, the relationship between the count outcome and one or more predictor variables is expressed using a logarithmic link function. This allows researchers to estimate how changes in the predictors affect the expected count of events. It is commonly applied in fields like epidemiology, economics, and social sciences to analyze event rates and incidence data.