From the course: Advanced and Specialized Statistics with Stata
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Negative binomial models - Stata Tutorial
From the course: Advanced and Specialized Statistics with Stata
Negative binomial models
- [Instructor] Let's talk about the negative binomial regression model for count data. This model is frequently used when we encounter over-dispersed count data. In other words, it deals with count data where there's more variation than would be expected from a Poisson process. However, negative binomial models are not helpful in cases of under-dispersion. This is because Poisson and negative binomial models arise from situations where events are independently generated. Over-dispersion happens if some causes of the Poisson process are known. Under-dispersion happens if count events are in some way connected or regulated and therefore, not independent. A negative binomial regression model is similar to a Poisson regression model, except that it has an extra parameter which allows the variance of the predictor counts to be higher. This parameter is called alpha and estimates to what extent over-dispersion is present…
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