Regresi Generalized Poisson Untuk Memodelkan Jumlah Penderita Gizi Buruk Pada Balita Di Surabaya

Abstract

The expansion of Poisson regression model which is used to solve the underdispersion data or overdispersion data known as Generalized Poisson (GP) regression model. The purpose of this final project is getting the parameter estimator of generalized linear model with response for GP distribution using maximum likelihood. This GP regression model can be applied on the data of number of Marasmus Kwashiorkorpatients in 25 subdistrict in Surabaya city in 2010. The variable response is the number of Marasmus Kwashiorkor patients, where as the predictor responses are the number of people who married at early age , the number of family heads who not graduated elementary school, the number of children who participated posyandu, the number of medical , the number of visits BKIA, and the number of poor population . The result of the GP regression model with statistic test can be concluded that the number of Marasmus Kwashiorkor patientsaffected by the number of visits BKIA and education levels of parents.The expansion of Poisson regression model which is used to solve the underdispersion data or overdispersion data known as Generalized Poisson (GP) regression model. The purpose of this final project is getting the parameter estimator of generalized linear model with response for GP distribution using maximum likelihood. This GP regression model can be applied on the data of number of Marasmus Kwashiorkorpatients in 25 subdistrict in Surabaya city in 2010. The variable response is the number of Marasmus Kwashiorkor patients, where as the predictor responses are the number of people who married at early age , the number of family heads who not graduated elementary school, the number of children who participated posyandu, the number of medical , the number of visits BKIA, and the number of poor population . The result of the GP regression model with statistic test can be concluded that the number of Marasmus Kwashiorkor patientsaffected by the number of visits BKIA and education levels of parents.

Analisis Faktor-Faktor Yang Mempengaruhi Stunting Pada Balita Di Kota Gorontalo Menggunakan Regresi Binomial Negatif

“…Hal ini berdampak pada nilai standar eror yang menjadi underestimate, sehingga kesimpulannya menjadi tidak valid. Beberapa penelitian yang terkait dengan penyelesaian masalah overdispersion diantaranya adalah model regresi generalized poisson (Mahfudhotin 2020) dan regresi binomial negatif (Sauddin et al 2020). Menurut penelitian (Prahutama et al 2017) bahwa salah satu pilihan yang terbaik adalah menggunakan regresi binomial negatif.…”