PEMODELAN REGRESI BERGANDA DAN GEOGRAPHICALLY WEIGHTED REGRESSION PADA TINGKAT PENGANGGURAN TERBUKA DI JAWA TENGAH

*Tiani Wahyu Utami  -  Jurusan Statistika, Universitas Muhammadiyah Semarang, Indonesia
Abdul Rohman  -  Jurusan Statistika, Universitas Muhammadiyah Semarang, Indonesia
Alan Prahutama scopus  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Published: 30 Dec 2016.
Open Access Copyright (c) 2016 MEDIA STATISTIKA
License URL: http://creativecommons.org/licenses/by-nc-sa/4.0/

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Abstract

The problems in employment was the growing number of Open Unemployment Rate (OUR). The open unemployment rate is a number that indicates the number of unemployed to the 100 residents are included in the labor force. The purpose of this study is mapping the data OUR in Central Java and the suspect and identify linkages between factors that cause OUR in the District / City of Central Java in 2014. Factors that allegedly include population density (X1), Inflation (X2), the GDP value (X3), UMR Value (X4), the percentage of GDP growth rate (X5), Hope of the old school (X6), the percentage of the labor force by age (X7) and the percentage of employment (X8). Geographically Weighted Regression (GWR) is a method for modeling the response of the predictor variables, by including elements of the area (spatial) into the point-based model. This research resulted in the conclusion that the OLS regression models have poor performance because the residual variance is not homogeneous. There were no significant differences between GWR models with OLS model or in other words generally predictor variables did not affect the response variable (rate of unemployment in Central Java) spatially. However, GWR model could captured modelling in each region.

 

Keywords: multiple linear regression, geographiically weighted regression, open unemployement rate in Central Java.

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