Article Sidebar

Submitted
April 11, 2016
Published
December 17, 2015
Download
PDF
Statistic
Read Counter : 1030 Download : 1080

Main Article Content

Abstract

Regresi terboboti geografi adalah salah satu model yang digunakan dalam regresi spasial. Model tersebut digunakan ketika regresi spasial mengalami masalah heteroskedastisitas. Setiap lokasi akan memiliki model yang berbeda-beda tergantung dari signifikansi koefisien regresi yang ada pada setiap variable disetiap lokasi. Kemudian jika tipe data dari variabel respon adalah biner maka akan disebut sebagai regresi logistik biner terboboti geografi. Penelitian ini bertujuan untuk menerapkan model tersebut pada kasus riel. Data yang digunakan diambil dari survei PODES yang terdiri dari satu variabel respon bertipe biner dan tiga variabel penjelas bertipe rasio. Variabel respon tersebut adalah kategori kemiskinan pada setiap kabupaten dan variabel penjelasnya adalah persentase desa perdagangan, persentase desa jasa dan rasio sekolah perdesa. Dari penelitian ini menghasilkan dua jenis model yaitu model global dan model lokal. Pada model global hanya dua variabel penjelas yang signifikan berpengaruh terhadap kategori kemiskinan di Pulau Jawa dengan nilai AIC sebesar 122.563. Pada Model Lokal menghasilkan hasil yang berbeda tergantung dari koefisien yang signifikan pada setiap kabupaten.

Keywords

GWR Regresi Logistik Biner Terboboti Geografi Fixed Bi-Square Model Global Model Lokal

Article Details

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Author Biography

Tuti Purwaningsih, Program Studi Statistika, Universitas Islam Indonesia


How to Cite
Purwaningsih, T. (2015). Penerapan Regresi Logistik Biner Terboboti Geografi dengan Pembobot Fixed Bi-Square (Geographically Weighted Binary Logistic Regression with Fixed Bi-Square Weight). EKSAKTA: Journal of Sciences and Data Analysis, 15(1-2), 48–54. https://doi.org/10.20885/eksakta.vol15.iss1-2.art5
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Agresti A. 2007. Categorical Data Analysis. New Jersey: John Wiley and Sons.
  2. Aidi MN, Purwaningsih T. 2013. Modelling Spatial Ordinal Logistic Regression and The Principal Component to Predict Poverty Status of Districts in Java Islandâ€, International Journal of Statistics and Aplication 3(1):1-8
  3. Anselin, L. 1998. Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic Publishers.
  4. Brunsdon, C., Fotheringham A.S., Charlton M.1999. Some notes on parametric significancetests for geographically weighted regression, Journal of Regional Science, Vol. 39, No 3, 497-524
  5. Cressie Noel A C. 1993. Statistics for Spatial Data. Revised Edition. John Wiley & Sons, Inc.
  6. Drapper, N.R, & Smith, H. 1998. Applied Regression Analysis 3rd Edition. Canada : John Wiley and Sons, Inc.
  7. Fotheringham A.S., Brunsdon C., Chartlon M. (2002). Geographically Weighted Regression, the analysis of spatially varying relationships, John Wiley and Sons, LTD.
  8. Fotheringham AS, Rogerson PA. 2009. Spatial Analysis. London: Sage Publications, Inc.
  9. Garson GD. LogisticRegression. [link] http://www.chass.ncsu.edu [11 April 2015].
  10. Haining Robert. 2004. Spatial Data Analysis Theory and Practice. Cambridge University Press.
  11. Hosmer DW, Lemeshow S. 2000. Applied Logistic RegressionSecond Edition. New York : John Wiley and Sons.
  12. Lee J, Wong DWS. 2001. Statistic for Spatial Data. New York : John Wiley & Sons, Inc.
  13. Lee J, Wong DWS. 2001. Statistical Analysis ArchView GIS. New York: John Wiley & Sons, Inc.
  14. Leung Y., Mei Chang L., and ZhangWen X. 2000. Statistical Test for Spatial Non Stationarity based on the geographically Weighted Regression Model, Environment and Planning, vol. 32, 9-32.
  15. Purwaningsih T, et.al. 2015. Comparison of Unifrom and Kernel Gaussian Weight Matrix in Generalized Spatial Panel Data Model. Open Journal of Statistics 5, 90-95.
  16. Ward MD, Gleditsch KS. 2008. Spatial Regression Models. Los Angeles: Sage Publications, Inc.