# PENERAPAN REGRESI QUASI-LIKELIHOOD PADA DATA CACAH (COUNT DATA) YANG MENGALAMI OVERDISPERSI DALAM REGRESI POISSON

• DESAK PUTU PRAMI MEITRIANI Faculty of Mathematics and Natural Sciences, Udayana University
• KOMANG GDE SUKARSA Faculty of Mathematics and Natural Sciences, Udayana University
• I PUTU EKA NILA KENCANA Faculty of Mathematics and Natural Sciences, Udayana University

### Abstract

Poisson regression can be used to analyze count data, with assuming equidispersion. However, in the case of overdispersion often occur in the count data. The implementation of Poisson Regression can not be applied on this data because the data having overdispersion, that will lead to underestimate the standard error. Thus, use Quasi-Likelihood regression on this data. Quasi-Likelihood regression was also could not handle the overdispersion, but Quasi-Likelihood regression can improve the value of the standard error becomes greater than the value of the standard error on Poisson regression. Thus, by using the Quasi-Likelihood regression obtained three independent variables that affect the number of divorce cases in each urban city of Denpasar in 2011.

### Author Biographies

DESAK PUTU PRAMI MEITRIANI, Faculty of Mathematics and Natural Sciences, Udayana University
Jurusan Matematika, FMIPA Universitas Udayana
KOMANG GDE SUKARSA, Faculty of Mathematics and Natural Sciences, Udayana University
Jurusan Matematika, FMIPA Universitas Udayana
I PUTU EKA NILA KENCANA, Faculty of Mathematics and Natural Sciences, Udayana University
Jurusan Matematika, FMIPA Universitas Udayana
Published
2013-05-31
How to Cite
PRAMI MEITRIANI, DESAK PUTU; SUKARSA, KOMANG GDE; KENCANA, I PUTU EKA NILA. PENERAPAN REGRESI QUASI-LIKELIHOOD PADA DATA CACAH (COUNT DATA) YANG MENGALAMI OVERDISPERSI DALAM REGRESI POISSON. E-Jurnal Matematika, [S.l.], v. 2, n. 2, p. 37-41, may 2013. ISSN 2303-1751. Available at: <https://ojs.unud.ac.id/index.php/mtk/article/view/6290>. Date accessed: 03 oct. 2023. doi: https://doi.org/10.24843/MTK.2013.v02.i02.p036.
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### Keywords

Poisson Regression; Overdispersion; Quasi-Likelihood Regression; standard error

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