Negative Binomial Regression
Definition
Negative binomial regression models count values with particularly large fluctuations.
A regression model for count data with greater variance than the Poisson model.
Simply explained
The method describes counts that fluctuate more strongly than a simpler count model expects.
Why it matters in practice
Helps realistically assess model quality, limits, and technical decisions.
Example from business
Support cases per week are modeled with seasonality and customer count.
Limits and pitfalls
- Use without checking the distribution or ignore exposure and many zeros.
Origin & history
The model is established in statistics and biometrics as a treatment of overdispersion.
Use in mid-sized companies
- Possible application: Support cases per week are modeled with seasonality and customer count.
Frequently asked questions
What does negative binomial regression mean?
A regression model for count data with greater variance than the Poisson model.
Which mistakes should be avoided?
Use without checking the distribution or ignore exposure and many zeros.
Sources
Source verified on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: