Negative Binomial Regression

Negative Binomial Regression is a statistical method used to analyze count data—like the number of emails received or accidents at a site—that often have variability higher than what simple models assume (called overdispersion). It helps us understand how different factors influence the number of occurrences by considering the extra randomness or unpredictability in the data. Unlike basic count models, it accounts for this extra variability, providing more accurate and reliable estimates of relationships between variables, making it valuable in fields like epidemiology, insurance, and social sciences.