Analysis of Factors Influencing the Percentage of Poverty in North Sumatra Using Robust Linear Regression

Liyanti Angun Silaban; Susiana

doi:10.55927/fjst.v2i2.2857

Authors

Liyanti Angun Silaban Universitas Negeri Medan
Susiana Universitas Negeri Medan

DOI:

https://doi.org/10.55927/fjst.v2i2.2857

Keywords:

Poverty Percentage, OLS, Outliers, M.Estimated Robust Regression

Abstract

The percentage of poverty in North Sumatra Province in 2021 will reach 9.01%. The data used are poverty percentage data in North Sumatra for 2021, Gini ratio, Covid-19 pandemic, poverty depth index, education and unemployment rate are considered to contribute to the percentage of poverty in North Sumatra Province. The purpose of this research is to determine the variables that have a significant effect on the Percentage of Poverty in North Sumatra in 2021. The method used is Ordinal Least Square. The results of this study show that the Poverty Percentage model in North Sumatra in 2021 uses a robust M-estimation regression which is 4.432790; 0.07230 and 0.007035. Partially, the regression coefficient shows that the poverty depth index has a significant effect on the percentage of poverty with a probability of 9.087. Simultaneously, the Gini ratio, the Covid-19 pandemic, the poverty depth index, education and unemployment affect the percentage of poverty with a probability of 64,602.

Downloads

Download data is not yet available.

References

Ali S. Hadi, Imon, A. H. M. R. and Werner, M. (2009) ‘Detection of Outliers’, WIREs Computational Statistics, 1(1), pp. 57–70.

BPS. (2021). Data dan Informasi Kemiskinan Kabupaten/kota Tahun 2021. Jakarta: BPS

Chen, C. (2002). Robust Regression and Outlier Detection with the ROBUSTREG Procedure. Cary NC: SAS Institute Inc.

Ferezagia, D. V. Analisis Tingkat Kemiskinan di Indonesia. Jurnal Sosial Humaniora Terapan. 1(1). 2018.

Li, S. Z., Wang, H., and Soh, W. Y. C. Robust Estimation of Rotation Angle from Image Sequences Using the Annelling M Estimator. Journal of Mathematical Imaging and Vision. 8(2): 181-192. 1998.

Montgomery, D. C. and Peck, E. A. Introduction to Linear Regression Analysis. John Wiley & Sons Inc. New York. 2006.

Pratiwi, H., Susanti, Y., & Handajani, S. S. (2018). A Robust Regression by Using Huber Estimator and Tukey Bisquare Estimator for Predicting Availability of Corn in Karanganyar Regency, Indonesia. Indonesian Journal of Applied Statistics, 1(1), 37-44.

Situmorang, Melva Hilda Stephanie, and Yuliana Susanti. "Pemodelan indeks keparahan kemiskinan di indonesia menggunakan analisis regresi robust." Indonesian Journal of Applied Statistics 3.1 (2020): 51-63.

Wardani, Intan Kusuma, Yuliana Susanti, and Sri Subanti. "Pemodelan Indeks Kedalaman Kemiskinan di Indonesia menggunakan Analisis Regresi Robust." Prosiding Snast (2021): 15-23

Wiguna, V. I., & Sakti, R. K. (2012). Analisis Pengaruh PDRB, Pendidikan, dan Pengangguran Terhadap Kemiskinan di Provinsi Jawa Tengah Tahun 2005-2010. Jurnal Ilmiah Mahasiswa FEB, 1(2).

Yacoub, Y. (2013). Pengaruh tingkat pengangguran terhadap tingkat kemiskinan Kabupaten/Kota di Provinsi Kalimantan Barat.