Geographically Weighted Ridge Regression dalam Kasus Multikolinearitas Pada Indeks Pembangunan Manusia di Kabupaten/Kota Provinsi Jawa Timur

Abstract

Linear Regression Analysis is a statistical method for modeling relation between two variable, response and explanatory variable. Geograpically Weighted Regression (GWR) is the development of linier regression analysis if the case of spatial divers case. Local multicollinearity is a condition when explanatory variables had correlated with each observation location. Geograpically Weighted Ridge Regression (GWRR) is a method used to model data containing local multicollinearity on spatial data. GWRR model was developed from ridge regression by adding weight as additional information. The study aims to model spatial data containing local multicollinearity to the Human Development Index (HDI) in the districts/municipalities of eastern Java Province in 2015. The result of this study was indicate that the indicator of the average length of school is a dominant indicator that  affects HDI.


 

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Author Biographies

Yani Arthayanti, Udayana University

Program Studi Matematika, Fakultas MIPA – Universitas Udayana

I Gusti Ayu Made Srinadi, Udayana University

Program Studi Matematika, Fakultas MIPA – Universitas Udayana

G.K. Gandhiadi, Udayana University

Program Studi Matematika, Fakultas MIPA – Universitas Udayana

 

Published
2017-12-30
How to Cite
ARTHAYANTI, Yani; SRINADI, I Gusti Ayu Made; GANDHIADI, G.K.. Geographically Weighted Ridge Regression dalam Kasus Multikolinearitas Pada Indeks Pembangunan Manusia di Kabupaten/Kota Provinsi Jawa Timur. Jurnal Matematika, [S.l.], v. 7, n. 2, p. 124-131, dec. 2017. ISSN 2655-0016. Available at: <https://ojs.unud.ac.id/index.php/jmat/article/view/37030>. Date accessed: 03 dec. 2022. doi: https://doi.org/10.24843/JMAT.2017.v07.i02.p89.
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Articles

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