The Comparison Of Generalized Poisson Regression And Negative Binomial Reression Methods In Overcoming Overdispersion

Abstract Data on the number of cervical cancer cases are discrete data count which are usually analyzed with Poisson regression. The characteristics of the Poisson regression mean and variance must be the same whereas in fact the count data is often becoming variance greater than the mean which is often referred to over dispersion. To deal with the problem over dispersion modelling can be done with Generalized Poisson Regression GPR and a Negative Binomial Regression because it does not require the mean value equal to the value of variance. Model GPR produces AIC value of 317.70. While the negative binomial regression models produced by AIC value 312.43. Then the best model is obtained from the negative binomial regression model because it produces the smallest AIC value.