PEMODELAN JUMLAH PENDUDUK MISKIN DI INDONESIA MENGGUNAKAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION

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dc.contributor.authorNatashia, Puji
dc.contributor.supervisorAdnan, Arisman
dc.date.accessioned2021-09-27T03:54:50Z
dc.date.available2021-09-27T03:54:50Z
dc.date.issued2021-01
dc.description.abstractPoverty is a condition of an individual’s inability to meet his basic needs. The number of poor people in Indonesia in September 2019 was 24,79 million people or around 9,22%. This study uses a geographically weighted negative binomial regression model to determine the factors that affect the amount of poverty in Indonesia in 2019. Modeling the number of poor people in Indonesia using Poisson regression is overdispersed, so to overcome it using geographically weighted negative binomial regression. Based on the regression model used, the results show that the significant variables are the open unemployment rate, the percentage of households that occupy non-private houses, the percentage of illiteracy, and gross regional domestic product at current prices per capita.en_US
dc.description.sponsorshipJurusan Matematika Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Riauen_US
dc.identifier.otherwahyu sari yeni
dc.identifier.urihttps://repository.unri.ac.id/handle/123456789/10222
dc.language.isoenen_US
dc.subjectPovertyen_US
dc.subjectgeographically weighted negative binomial regressionen_US
dc.subjectPoisson regressionen_US
dc.subjectoverdispersionen_US
dc.subjectnegative binomial regressionen_US
dc.titlePEMODELAN JUMLAH PENDUDUK MISKIN DI INDONESIA MENGGUNAKAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSIONen_US
dc.typeArticleen_US

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