# PENERAPAN REGRESI BINOMIAL NEGATIF UNTUK MENGATASI OVERDISPERSI PADA REGRESI POISSON

• PUTU SUSAN PRADAWATI Faculty of Mathematics and Natural Sciences, Udayana University
• KOMANG GDE SUKARSA Faculty of Mathematics and Natural Sciences, Udayana University
• I GUSTI AYU MADE SRINADI Faculty of Mathematics and Natural Sciences, Udayana University

### Abstract

Poisson regression was used to analyze the count data which Poisson distributed. Poisson regression analysis requires state equidispersion, in which the mean value of the response variable is equal to the value of the variance. However, there are deviations in which the value of the response variable variance is greater than the mean. This is called overdispersion. If overdispersion happens and Poisson Regression analysis is being used, then underestimated standard errors will be obtained. Negative Binomial Regression can handle overdispersion because it contains a dispersion parameter. From the simulation data which experienced overdispersion in the Poisson Regression model it was found that the Negative Binomial Regression was better than the Poisson Regression model.

### Author Biographies

PUTU SUSAN PRADAWATI, 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 GUSTI AYU MADE SRINADI, Faculty of Mathematics and Natural Sciences, Udayana University
Jurusan Matematika, FMIPA Universitas Udayana
Published
2013-09-02
How to Cite
PRADAWATI, PUTU SUSAN; SUKARSA, KOMANG GDE; SRINADI, I GUSTI AYU MADE. PENERAPAN REGRESI BINOMIAL NEGATIF UNTUK MENGATASI OVERDISPERSI PADA REGRESI POISSON. E-Jurnal Matematika, [S.l.], v. 2, n. 2, p. 6-10, sep. 2013. ISSN 2303-1751. Available at: <https://ojs.unud.ac.id/index.php/mtk/article/view/6285>. Date accessed: 10 june 2023. doi: https://doi.org/10.24843/MTK.2013.v02.i02.p031.
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### Keywords

Poisson Regression; Overdispersion; Negative Binomial Regression; best model