Poisson Regression is a statistical technique used to model count data, particularly when the counts represent the number of times an event occurs in a fixed interval of time or space. It assumes that the data follows a Poisson distribution, which is characterized by its mean being equal to its variance. This method is useful for predicting the occurrence of rare events, such as the number of accidents at a traffic intersection. In Poisson Regression, the relationship between the dependent variable (the count) and one or more independent variables (predictors) is expressed using a logarithmic link function. This allows for the estimation of how changes in the predictors affect the expected count of events. It is commonly applied in fields like epidemiology, finance, and social sciences.