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All Journal International Journal of Quantitative Research and Modeling Priviet Social Sciences Journal
Nur Rezky Safitriani
Tadulako University
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Physics Social Sciences

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Comparison of Poisson and Negative Binomial Regression Models in Identifying Factors Influencing Covid-19 Deaths in Indonesia. Nabilla Rida Tri Nisa; Amanatullah Pandu Zenklinov; Husna Afanyn Khoirunissa; Nur Rezky Safitriani; Erlyne Nadhilah Widyaningrum; Rizka Amalia Putri; Morina A. Fathan
International Journal of Quantitative Research and Modeling Vol. 6 No. 4 (2025): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i4.1126

Abstract

This research compares Poisson Regression and Generalized Negative Binomial (GNB) Regression to underscore the factors that influence the growth of COVID-19 deaths in Indonesia. Count data such as mortality cases often violates the Poisson assumption of equidispersion (null mean equals variance) causing overdispersion. The GNB model is suggested as a remedy for overdispersed data crime prevention has become increasingly necessary for systematic development because secondary data from the Indonesian government has included dependable variables such as mortality rates for people aged over 60, diabetes mellitus, heart disease, lung disease, healthcare worker percentages, referral hospitals, and the population. The Poisson Regression reported R² of 87.67% and experienced overdispersion (θ₁ = 356.27, θ₂ = 417,597). The GNB model, in contrast, with a lower AIC (499.5566), overtook Poisson. Important factors that had significant impact on both models were mortality rates for individuals over 60, diabetes mellitus, healthcare workers, and referral hospitals, whereas heart and lung disease mortality rates were the ones that were not material. The GNB model had a better fit and tackled the issues of overdispersion in the Poisson Regression.
Analysis of macroeconomic factors affecting poverty levels in Indonesia using a dummy regression model approach Nur Rezky Safitriani; Valina Ambarwati; Miftahull Jannah; Nabila Safitri
Priviet Social Sciences Journal Vol. 5 No. 8 (2025): August 2025
Publisher : Privietlab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55942/pssj.v5i8.630

Abstract

Poverty in Indonesia remains a complex macroeconomic issue, influenced by various social, economic, and regional disparities. This study employed a dummy variable regression model to analyze the factors affecting poverty more comprehensively, allowing for the identification of categorical geographic effects. This study examines the influence of the Gender Empowerment Index, Expected Years of Schooling, Gini Ratio, Open Unemployment Rate, and Formal Employment on the percentage of the poor population in Indonesia, while considering regional classifications in Western, Central, and Eastern Indonesia. The results show that the Gender Empowerment Index and proportion of Formal Employment have a significant negative effect on poverty, while the Gini Ratio has a significant positive effect. Additionally, the Western and Central regions exhibit significantly lower poverty rates than the eastern region. The dummy regression model explains 83,64% of the variation in poverty across provinces, making it a relevant basis for formulating region-specific and macro-economically informed poverty alleviation policies.
Co-Authors Amanatullah Pandu Zenklinov Erlyne Nadhilah Widyaningrum Husna Afanyn Khoirunissa Miftahull Jannah Morina A. Fathan Nabila Safitri Nabilla Rida Tri Nisa Rizka Amalia Putri Valina Ambarwati