CD Skripsi

Model Regresi Poisson Berbobot Secara Geografis Pada Kasus Demam Berdarah Dengue Di Indonesia Tahun 2015

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The geographically weighted Poisson regression model which is abbreviated as GWPR is a local form of Poisson regression which is applied to spatial data, where the location noticed which assumes that the data has a Poisson distribution. Poisson regression is a nonlinear regression model in which the response variables follow the Poisson distribution. Poisson regression modeling in this study uses spatial data, which means that data containing information about the location or geographic location of an area that is influenced by data measurements at other locations, has a certain coordinate system. The purpose of this study was to determine the GWPR model and the factors that influence the number of dengue fever cases in Indonesia in 2015. The spatial weighting used is the Gaussian kernel function and the optimum bandwidth. The data used in this study is secondary data, namely the number of cases of dengue hemorrhagic fever (DHF) in Indonesia in 2015. The parameter estimation method for the GWPR model is Maximum Likelihood Estimate (MLE). The results of this study indicate that the maximum likelihood estimator is obtained using the Newton-Raphson iteration method and the factors that influence the number of DHF cases in Indonesia are different (local). Locally influencing factors are population density, number of health workers, number of health facilities, and amount of rainfall.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

1703123238

Penerbit

Pekanbaru : Universitas Riau – FMIPA – STATISTIKA., 2022

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

1703123238

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

STATISTIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

NADYA

Versi lain/terkait

Lampiran Berkas

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