Like Poisson Regression, it also deals with count data. The question arises “how it is different from poisson regression”. The answer is negative binomial regression does not assume distribution of count having variance equal to its mean. While poisson regression assumes the variance equal to its mean.
When the variance of count data is greater than the mean count, it is a case of overdispersion. The opposite of the previous statement is a case of under-dispersion.
nb.model <- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine) summary(nb.model)