ANALISIS GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN PEMBOBOT KERNEL BI-SQUARE UNTUK ANGKA PENGANGGURAN DI KABUPATEN BOJONEGORO

Abstract View: 365, PDF Download: 312

Authors

  • Alif Yuanita Kartini UNUGIRI Bojonegoro

DOI:

https://doi.org/10.32665/james.v2i1.75

Keywords:

unemployment, Spatial, Geographically Weighted Regression, Kernel Bi-square, Pengangguran, Spasial

Abstract

References

[1] Utami, T.W., dkk. Pemodelan Regresi Berganda dan Geographically Weighted Regression pada Tingkat Pengangguran Terbuka di Jawa Tengah. Media Statistika 9(2) (2016).
[2] Prasanti, T.A. Aplikasi Regresi Data Panel untuk Pemodelan Tingkat Pengangguran Terbuka Kabupaten/Kota di Provinsi Jawa Tengah.Skripsi Program Sarjana Statistika Universitas Diponegoro (2015).
[3] Fotheringham, A.S. Brunsdon, C., dan Charlton, M. Geographically Weighted Regression, John Wiley and Sons, Chichester, UK. (2002).
[4] Leung, Y. Mei, C.L. dan Zhang, W.X. Statistical Tests for Spatial Non-Stationarity Based on the Geographically Weighted Regression Model, Environment and Planning A (2000).
[5] Draper, N. dan Smith, H. Analisis Regresi Terapan. PT. Gramedia Pustaka Utama. Jakarta (1992).
[6] Mei, C.L. “Geographically Weighted Regression Technique for Spatial Data Analysis”, School Of Science Xi’an Jiaotong University (2005).
[7] Gujarati, D. Dasar-dasar Ekonometrika Edisi Lima (diterjemahkan oleh Mangunsong, R.C.). Salemba Empat. Jakarta (2012).

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Published

2019-04-25

How to Cite

[1]
A. Y. Kartini, “ANALISIS GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN PEMBOBOT KERNEL BI-SQUARE UNTUK ANGKA PENGANGGURAN DI KABUPATEN BOJONEGORO”, JaMES, vol. 2, no. 1, pp. 51–59, Apr. 2019.

Issue

Vol. 2 No. 1 (2019): Journal of Mathematics Education and Science

Section

Articles

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Abstract View: 365, PDF Download: 312