PERBANDINGAN REGRESI BINOMIAL NEGATIF DAN REGRESI GENERALISASI POISSON DALAM MENGATASI OVERDISPERSI (Studi Kasus: Jumlah Tenaga Kerja Usaha Pencetak Genteng di Br. Dukuh, Desa Pejaten)

NI MADE RARA KESWARI (Faculty of Mathematics and Natural Sciences, Udayana University)
I WAYAN SUMARJAYA (Faculty of Mathematics and Natural Sciences, Udayana University)
NI LUH PUTU SUCIPTAWATI (Faculty of Mathematics and Natural Sciences, Udayana University)

E-Jurnal Matematika
Vol 3 No 3 (2014)

Poisson regression is a nonlinear regression that is often used to model count response variable and categorical, interval, or count regressor. This regression assumes equidispersion, i.e., the variance equals the mean. However, in practice, this assumption is often violated. One of this violation is overdispersion in which the variance is greater than the mean. There are several  methods to overcome overdispersion. Two of these methods are negative binomial regression and generalized Poisson regression. In this research, binomial negative regression and generalized Poisson regression statistically equally good in handling overdispersion.