Open AccessPERBANDINGAN REGRESI BINOMIAL NEGATIF DAN REGRESI GENERALISASI POISSON DALAM MENGATASI OVERDISPERSI (Studi Kasus: Jumlah Tenaga Kerja Usaha Pencetak Genteng di Br. Dukuh, Desa Pejaten)
Author(s) -
Ni Made Rara Keswari,
I Wayan Sumarjaya,
Ni Luh Putu Suciptawati
Publication year - 2014
Publication title -
e-jurnal matematika
Language(s) - English
Resource type - Journals
ISSN - 2303-1751
DOI - 10.24843/mtk.2014.v03.i03.p072
Subject(s) - overdispersion , mathematics , statistics , negative binomial distribution , poisson regression , count data , quasi likelihood , poisson distribution , zero inflated model , regression analysis , econometrics , population , demography , sociology
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.