Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Research Review: Jurnal Ilmiah Multidisiplin
Agnalia Sintasya Putri
Universitas Negeri Gorontalo
Humanities Education Languange, Linguistic, Communication & Media Social Sciences Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Regresi Robust Method Of Moment dan Weighted Least Square dalam Mengatasi Masalah Pencilan dan Heterokedastisitas (Studi Kasus: Tingkat Pengangguran Terbuka di Indonesia Tahun 2022) Agnalia Sintasya Putri; Armayani Arsal
Research Review: Jurnal Ilmiah Multidisiplin Vol. 4 No. 1 (2025): Research Review: Jurnal Ilmiah Multidisiplin (Februari 2025 - Juli 2025)
Publisher : Transbahasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54923/researchreview.v4i1.120

Abstract

Multiple linear regression is a method that investigates the relationship between independent variables and dependent variables. The purpose of this study is to examine data that has outlier and heteroscedasticity problems. The method used in this research is the Robust Method of Moment Weighted Least Square. This study uses data on Open Unemployment Rate (Y), Total Population (X1), Human Development Index (X2), and Labor Force Participation Rate. The results of parameter estimation with the Ordinary Least Square (OLS) method obtained the initial data equation Ŷ= 4.824 - 4.729 + 4.291 + 3.559. The test results on the model can be seen that there is heteroscedasticity and outliers. Robust Method (Method Of Moment) Weighted Least Square analysis obtained a regression equation Ŷ = 0.09023 + 0.15395 + 0.26832 + 0.52220 with R 2 of 0.068 and MSE of 0.63. The test results on the model can be seen that there is no heteroscedasticity and the outlier problem has been reduced. This shows that the Robust (Method Of Moment) Weighted Least Square method is a method that can be used for outlier and heteroscedasticity problems in data.
Co-Authors Armayani Arsal