Modelling Of Accident Data In Nigeria: Poisson And Poisson Gamma Regressio
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2015
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Abstract
Poisson and Poisson-gamma (Negative Binomial) regression models belong to the group of generalized linear models that are suitable to model count data. While the two regression types often give similar results, there can be differences in the effects of the covariates they estimated depending on weather the data is equi-dispersed, over-dispersed or under-dispersed. This work therefore demonstrates the behaviours of Poisson and Poisson-gamma regression models when the data is overdispersed (variance greater than the mean). The two regression types were employed to model the number of people killed in road accidents as a function of the number of fatal, serious and minor accidents that occurred. Results from the two regression models indicated that number of people killed in road accidents is determined majorly by the number of fatal and serious cases of road accidents that occurred and not really by the number of minor accidents. The differences in the two regression models are described in the light of the over-dispersion in the data and the ability of the models to account for it. Road accident data of Kwara State, Nigeria for nine consecutive years from 2000 to 2008, primarily collected by the Nigeria Police Force (NPF), were employed in this study.
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Aderoju, S.A. and Jolayemi, E.T. (2015). Modelling of accident data in Nigeria: Poisson and Poisson-Gamma regression. International Journal of Scientific Research and Engineering Studies. Vol.2, Issue 2, pp. 23-29.