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Negative Binomial Regression Models . Negative binomial regression is used to model count data for which the variance is higher than the mean. The possible values of y are the nonnegative integers: Negative binomial regression model of factors associated from www.researchgate.net Then e(y) = pr (1 p) var(y) = pr (1 p)2 = + 1 r 2 hence our assumption on the variance in the test for overdispersion. Negative binomial regression is a popular generalization of poisson regression because it loosens the highly restrictive assumption that the variance is equal to the mean made by the poisson model. Basic properties of the negative binomial distribution fitting the negative binomial model basic properties of the negative binomial dist.

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