An Empirical Study of Generalized Linear Model for Count Data
- *Corresponding Author:
- Abdulkabir M
Statistics Department, University of Ilorin
E-mail: [email protected]
Received date: April 16, 2015; Accepted date: August 17, 2015; Published date: August 26, 2015
Citation: Abdulkabir M, Edem UA, Tunde RS, Kemi BL (2015) An Empirical Study of Generalized Linear Model for Count Data. J Appl Computat Math 4:253. doi:10.4172/2168-9679.1000253
Copyright: © 2015 Abdulkabir M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
This paper deals with an empirical study of generalized linear model (GLM) for count data. In particular, Poisson regression model which is also known as generalized linear model for Poisson error structure has been widely used in recent years; it is also used in modeling of count and frequency data. Quasi Poisson model was employ for handling over and under dispersion which the data was found to be over dispersed and another way of handling over dispersion is negative binomial regression model. In this study, the two regression model were compare using the Akaike information criterion (AIC), the model with minimum AIC shows the best which implies the Poisson regression model.