%0 Thesis
%9 Skripsi
%A Salman Al-Farisi, NIM.: 19106010037
%B FAKULTAS SAINS DAN TEKNOLOGI
%D 2024
%F digilib:67399
%I UIN SUNAN KALIJAGA YOGYAKARTA
%K Regresi Poisson, Binomial Negatif, HBN, Overdispersi, MLE, AIC
%P 93
%T 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)
%U https://digilib.uin-suka.ac.id/id/eprint/67399/
%X 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)
%Z Pembimbing: Sri Utami Zuliana, S.Si.,M.Sc.,Ph.D