Penanganan Overdispersi pada Regresi Poisson dengan Regresi Binomial Negatif pada Kasus Kemiskinan di Indonesia

Abstract

Poisson regression can be used to model rare events that consist of count data. Poisson regression application is carried out to find out external factors that affect the number of poor people in Indonesia by the province in 2016. The assumptions that must be met in this analysis are equdispersion. However, in real cases there is often a problem of overdispersion, ie the value of the variance is greater than the average value. High diversity can be caused by outliers. Expenditures on outliers have not been able to deal with the problem of overdispersion in Poisson Regression. One way to overcome this problem is to replace the Poisson distribution assumption with the Negative Binomial distribution. The results of the analysis show that the Negative Binomial Regression model without outliers is better than the Poisson Regression without outliers model indicated by a smaller AIC value. Based on the Negative Binomial Regression model without this outlier the external factors that affect the number of poor people in Indonesia by the province in 2016 are the percentage of households with floor conditions of houses with soil by province, population by province, percentage of unemployment to the total workforce by province and the percentage of the workforce against the working age population.