MODELING LIFE EXPECTANCY IN CENTRAL JAVA USING SPATIAL DURBIN MODEL

Central Java in 2017 was one of the provinces with high life expectancy, ranking second. Life expectancy of Central Java Province in 2017 is 74.08% per year. The fields of education, health and socio-economics, are several factors that are thought to influence the life expectancy in an area. To find...

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Main Authors: Arief Rachman Hakim, Hasbi Yasin, Agus Rusgiyono
Format: Article
Language:English
Published: Universitas Diponegoro 2019-12-01
Series:Media Statistika
Online Access:https://ejournal.undip.ac.id/index.php/media_statistika/article/view/24668
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spelling doaj-10f9a20b18ed4a0cbf85294b85c7b6f72020-11-25T03:40:40ZengUniversitas DiponegoroMedia Statistika1979-36932477-06472019-12-0112215216310.14710/medstat.12.2.152-16316030MODELING LIFE EXPECTANCY IN CENTRAL JAVA USING SPATIAL DURBIN MODELArief Rachman Hakim0Hasbi Yasin1Agus Rusgiyono2Departemen Statistika, Universitas DiponegoroDepartemen Statistika, FSM, Universitas DiponegoroDepartemen Statistika, FSM, Universitas DiponegoroCentral Java in 2017 was one of the provinces with high life expectancy, ranking second. Life expectancy of Central Java Province in 2017 is 74.08% per year. The fields of education, health and socio-economics, are several factors that are thought to influence the life expectancy in an area. To find out what factors that the regression analysis method can use to find out what factors influence the life expectancy. But in observations found data that have a spatial effect (location) called spatial data, a spatial regression method was developed such as linear regression analysis by adding spatial effects. One form of spatial regression is Spatial Durbin Model (SDM) which has a form like the Spatial Autoregressive Model (SAR). The difference between the two if in the SAR model the effect of spatial lag taken into account in the model is only on the response variable (Y) but in the SDM method, effect of spatial lag on the predictor variable (X) and response (Y) are also taken into account. Selection of the best model using Mean Square Error (MSE), obtained by the MSE value of 1.156411, the number mentioned is relatively small 0, which indicates that the model is quite good.https://ejournal.undip.ac.id/index.php/media_statistika/article/view/24668
collection DOAJ
language English
format Article
sources DOAJ
author Arief Rachman Hakim
Hasbi Yasin
Agus Rusgiyono
spellingShingle Arief Rachman Hakim
Hasbi Yasin
Agus Rusgiyono
MODELING LIFE EXPECTANCY IN CENTRAL JAVA USING SPATIAL DURBIN MODEL
Media Statistika
author_facet Arief Rachman Hakim
Hasbi Yasin
Agus Rusgiyono
author_sort Arief Rachman Hakim
title MODELING LIFE EXPECTANCY IN CENTRAL JAVA USING SPATIAL DURBIN MODEL
title_short MODELING LIFE EXPECTANCY IN CENTRAL JAVA USING SPATIAL DURBIN MODEL
title_full MODELING LIFE EXPECTANCY IN CENTRAL JAVA USING SPATIAL DURBIN MODEL
title_fullStr MODELING LIFE EXPECTANCY IN CENTRAL JAVA USING SPATIAL DURBIN MODEL
title_full_unstemmed MODELING LIFE EXPECTANCY IN CENTRAL JAVA USING SPATIAL DURBIN MODEL
title_sort modeling life expectancy in central java using spatial durbin model
publisher Universitas Diponegoro
series Media Statistika
issn 1979-3693
2477-0647
publishDate 2019-12-01
description Central Java in 2017 was one of the provinces with high life expectancy, ranking second. Life expectancy of Central Java Province in 2017 is 74.08% per year. The fields of education, health and socio-economics, are several factors that are thought to influence the life expectancy in an area. To find out what factors that the regression analysis method can use to find out what factors influence the life expectancy. But in observations found data that have a spatial effect (location) called spatial data, a spatial regression method was developed such as linear regression analysis by adding spatial effects. One form of spatial regression is Spatial Durbin Model (SDM) which has a form like the Spatial Autoregressive Model (SAR). The difference between the two if in the SAR model the effect of spatial lag taken into account in the model is only on the response variable (Y) but in the SDM method, effect of spatial lag on the predictor variable (X) and response (Y) are also taken into account. Selection of the best model using Mean Square Error (MSE), obtained by the MSE value of 1.156411, the number mentioned is relatively small 0, which indicates that the model is quite good.
url https://ejournal.undip.ac.id/index.php/media_statistika/article/view/24668
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