ANALISIS REGRESI POISSON, REGRESI BINOMIAL NEGATIF DAN REGRESI HURDLE BINOMIAL NEGATIF PADA DATA OVERDISPERSI (STUDI KASUS : JUMLAH KASUS KEMATIAN PENDERITA DEMAM BERDARAH DENGUE DI PROVINSI JAWA TIMUR TAHUN 2022)

Salman Al-Farisi, NIM.: 19106010037 (2024) ANALISIS REGRESI POISSON, REGRESI BINOMIAL NEGATIF DAN REGRESI HURDLE BINOMIAL NEGATIF PADA DATA OVERDISPERSI (STUDI KASUS : JUMLAH KASUS KEMATIAN PENDERITA DEMAM BERDARAH DENGUE DI PROVINSI JAWA TIMUR TAHUN 2022). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

The Poisson regression aims to illustrates the connection between the predicting variable and the response variable which has to be a discreet data. The required assumption is equidispersion, which is the response variable variant that is equal to the mean. Yet, this assumption is often violated, in which the variant value is larger than the mean, this is called overdispersion. To solve this, the Binomial Negative regression and the Hurdle Negative Binomial (HNB) can be done. The parameter is estimated using the Maximum Likelihood Estimation (MLE) approach. The best model is chosen through the Akaike Information Criterion (AIC) method. All three regression analyses are applied to the Dengue Fever Death Toll in East Java Province 2022 with several factors that may affect it. Based on the AIC calculations, the model that has the best predicting ability is the Negative Binomial regression which is 191,94. The Negative Binomial regression model obtained is μi = exp(6.47067 + 0, 25272X1 − 0.04879X5 − 0.10942X6)

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