Handling overdispersion in poisson regression using negative binomial regression for poverty case in west java

Poverty is one of government's problems in West Java Province that should be suppressed even abolished considering its violates human rights to live in prosperity. In September 2019, number of Poor People reaches 3.38 million people (6.82 percent). Thus, the results of this paper expected could...

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Veröffentlicht in:Procedia computer science Jg. 216; S. 517 - 523
Hauptverfasser: Suryadi, Felina, Jonathan, Stanley, Jonatan, Kelvin, Ohyver, Margaretha
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 2023
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ISSN:1877-0509, 1877-0509
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Abstract Poverty is one of government's problems in West Java Province that should be suppressed even abolished considering its violates human rights to live in prosperity. In September 2019, number of Poor People reaches 3.38 million people (6.82 percent). Thus, the results of this paper expected could help government in overcome poverty problem in West Java Province by finding the best regression method and model to predict number of poor people and find the most influential predictor that affects the number of poor people in West Java Province. We used secondary datas in 2019 from BPS Jawa Barat with four predictor: Province Minimum Wage (UMP), Human Development Index (IPM), Open Unemployment Rate (TPT), and Number of House Hold (RumahTangga) and one respon variable: Number of Poor People in West Java. The conclusion of this paper are: overdispersion could be overcome and modelled better using Negative Binomial, and the significant variables (which means have impact for response variable) in this case are only two predictor: Human Development Index (IPM) and Number of House Hold (RumahTangga). Human Development Index (IPM) is the most impactable predictor that government should build up to decrease Number of Poor People, while Number of House Hold (RumahTangga) does not have a really big impact but if it get depress would decrease Number of Poor People in West Java.
AbstractList Poverty is one of government's problems in West Java Province that should be suppressed even abolished considering its violates human rights to live in prosperity. In September 2019, number of Poor People reaches 3.38 million people (6.82 percent). Thus, the results of this paper expected could help government in overcome poverty problem in West Java Province by finding the best regression method and model to predict number of poor people and find the most influential predictor that affects the number of poor people in West Java Province. We used secondary datas in 2019 from BPS Jawa Barat with four predictor: Province Minimum Wage (UMP), Human Development Index (IPM), Open Unemployment Rate (TPT), and Number of House Hold (RumahTangga) and one respon variable: Number of Poor People in West Java. The conclusion of this paper are: overdispersion could be overcome and modelled better using Negative Binomial, and the significant variables (which means have impact for response variable) in this case are only two predictor: Human Development Index (IPM) and Number of House Hold (RumahTangga). Human Development Index (IPM) is the most impactable predictor that government should build up to decrease Number of Poor People, while Number of House Hold (RumahTangga) does not have a really big impact but if it get depress would decrease Number of Poor People in West Java.
Author Jonathan, Stanley
Suryadi, Felina
Ohyver, Margaretha
Jonatan, Kelvin
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  surname: Ohyver
  fullname: Ohyver, Margaretha
  email: mohyver@binus.edu
  organization: Statistics Department, School of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia
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Cites_doi 10.25077/jmu.3.4.58-65.2014
10.1177/1536867X1201200412
10.1016/S0378-3758(01)00250-6
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Keywords Poisson Regression
Poverty
Overdispersion
Negative Binomial Regression
Language English
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Snippet Poverty is one of government's problems in West Java Province that should be suppressed even abolished considering its violates human rights to live in...
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StartPage 517
SubjectTerms Negative Binomial Regression
Overdispersion
Poisson Regression
Poverty
Title Handling overdispersion in poisson regression using negative binomial regression for poverty case in west java
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