Performance of Generalized Poisson Regression Model and Negative Binomial Regression Model in case of Over-dispersion Count Data

Abstract: This paper represents the comparison between Negative Binomial Regression model and Generalized Poisson Regression model for over-dispersion count data. For this comparison, we used BDHS 2007 data in where the response variable is the total children ever born which is a count data. When the response variable is count, then Poisson Regression Model as a Generalized Linear Model is widely and popularly used to analyze such type of response variable and Poisson Regression model gives better result than the usual regression model for analyzing count data. In this paper, the descriptive statistics of the total children ever born data exhibit the presence of over-dispersion in the data set. Since the total children ever born data used in this study exhibit over-dispersion, we can use Negative Binomial Regression Model and Generalized Poisson Regression Model. These two models have statistical advantages over standard Poisson regression model and are suitable for analysis of count data that exhibit either over-dispersion or under-dispersion.