Negative Binomial regression is a statistical method used to model count data, particularly when the data exhibit overdispersion. Overdispersion occurs when the variance of the data is greater than the mean, which can violate the assumptions of simpler models like Poisson regression. This technique helps researchers understand the relationship between a dependent count variable and one or more independent variables. In Negative Binomial regression, the model incorporates an additional parameter to account for the extra variability in the data. This makes it more flexible and suitable for various applications, such as analyzing the number of occurrences of an event, like disease outbreaks or accidents, where the data may not fit traditional models well.