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PEMODELAN JUMLAH KASUS KEMATIAN BAYI DI KABUPATEN KUANTAN SINGINGI MENGGUNAKAN REGRESI BINOMIAL NEGATIF

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dc.contributor.author Sari, Luci Manda
dc.date.accessioned 2023-06-21T02:35:41Z
dc.date.available 2023-06-21T02:35:41Z
dc.date.issued 2023-04
dc.identifier.citation Perpustakaan en_US
dc.identifier.other Elfitra
dc.identifier.uri https://repository.unri.ac.id/handle/123456789/11031
dc.description.abstract Infant mortality is one of the commonly used mortality indicators in Indonesia. Cases of infant mortality reflect the level of social welfare and health status of a nation for the realization of health development in a country. One of the ways to reveal the high number of infant mortality cases is by knowing the most significant factors that cause infant mortality. The results obtained from this study experienced overdispersion in Poisson regression, so that in the analysis using negative binomial regression, a significant variable was obtained, namely the number of medical personnel with the best AIC in negative binomial regression for a significant variable in reduction II with an AIC value of 60.42 en_US
dc.description.provenance Submitted by wahyu sari yeni (ayoe32@ymail.com) on 2023-06-21T02:35:41Z No. of bitstreams: 1 LUCI MANDA SARI_compressed.pdf: 228718 bytes, checksum: 3734fb1ccf2913c4302d3a3c43cfc643 (MD5) en
dc.description.provenance Made available in DSpace on 2023-06-21T02:35:41Z (GMT). No. of bitstreams: 1 LUCI MANDA SARI_compressed.pdf: 228718 bytes, checksum: 3734fb1ccf2913c4302d3a3c43cfc643 (MD5) Previous issue date: 2023-04 en
dc.description.sponsorship Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Riau en_US
dc.language.iso en en_US
dc.publisher Elfitra en_US
dc.subject Infant Mortality Cases en_US
dc.subject Poisson Regression en_US
dc.subject Overdispersion en_US
dc.subject Negative Binomal Regression en_US
dc.title PEMODELAN JUMLAH KASUS KEMATIAN BAYI DI KABUPATEN KUANTAN SINGINGI MENGGUNAKAN REGRESI BINOMIAL NEGATIF en_US
dc.type Article en_US
dc.contributor.supervisor Efendi, Rustam


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