Poisson Regression
Poisson regression is a statistical modeling technique used to analyze count data, where the response variable represents the number of occurrences of an event within a fixed interval. It assumes that the response variable follows a Poisson distribution and uses a log link function to model the relationship between the mean count and predictor variables. This method is particularly useful for handling over-dispersed or under-dispersed count data compared to standard linear regression.
Developers should learn Poisson regression when working with datasets involving count outcomes, such as the number of website visits per day, defect counts in manufacturing, or customer complaints per month. It is essential in fields like epidemiology (e.g., disease case counts), insurance (e.g., claim frequencies), and web analytics to model rare events or rates accurately, especially when data exhibits non-normal distributions or variance proportional to the mean.