BAREKENG: Jurnal Ilmu Matematika dan Terapan
Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan

NEGATIVE BINOMIAL REGRESSION AND GENERALIZED POISSON REGRESSION MODELS ON THE NUMBER OF TRAFFIC ACCIDENTS IN CENTRAL JAVA

M Al Haris (Jurusan Statistika, Fakultas MIPA, Universitas Muhammadiyah Semarang)
Prizka Rismawati Arum (Jurusan Statistika, Fakultas MIPA, Universitas Muhammadiyah Semarang)

Article Info

Publish Date
01 Jun 2022

Abstract

Traffic accidents that always increase along with the increasing population growth and the number of vehicles impact the national economy. The number of traffic accidents is a count data that a Poisson distribution can approximate. The Poisson regression model often found violations of the overdispersion assumption by modeling the factors that affect the number of traffic accidents. Alternative models proposed to overcome the emergence of overdispersion in the Poisson regression model are the Generalized Poisson Regression and Negative Binomial Regression Models. Based on the analysis results, it was found that the overdispersion assumption violates the Poisson regression model, and the Generalized Poisson regression model is the best because it has the smallest AIC value of 485.50. Factors that significantly affect the number of traffic accidents in Central Java Province are the percentage of adolescents and the percentage of accidents occurring in the road area of the district/city.

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Journal Info
BAREKENG: Jurnal Ilmu Matematika dan Terapan
Website

Abbrev

barekeng

Publisher

Universitas Pattimura

Subject

Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Description

BAREKENG: Jurnal ilmu Matematika dan Terapan is one of the scientific publication media, which publish the article related to the result of research or study in the field of Pure Mathematics and Applied Mathematics. Focus and scope of BAREKENG: Jurnal ilmu Matematika dan Terapan, as follows: - Pure ...