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 is common in real-world scenarios. This regression technique helps researchers understand the relationship between a dependent count variable and one or more independent variables. In Negative Binomial Regression, the response variable is typically a count, such as the number of events occurring in a fixed period. The model assumes that the counts follow a negative binomial distribution, allowing for greater flexibility in handling variability compared to simpler models like Poisson regression. This makes it a valuable tool in fields such as epidemiology and ecology.