Open AccessA Comparison of Weighted Least Square and Quantile Regression for Solving Heteroscedasticity in Simple Linear Regression
Author(s) -
Welly Fransiska,
Nugroho Sigit,
Ramya Rachmawati
Publication year - 2022
Publication title -
journal of statistics and data science
Language(s) - English
Resource type - Journals
ISSN - 2828-9986
DOI - 10.33369/jsds.v1i1.21011
Subject(s) - homoscedasticity , heteroscedasticity , quantile regression , mathematics , linear regression , statistics , estimator , minimum variance unbiased estimator , econometrics , regression analysis , quantile , simple linear regression
Regression analysis is the study of the relationship between dependent variable and one or more independent variables. One of the important assumption that must be fulfilled to get the regression coefficient estimator Best Linear Unbiased Estimator (BLUE) is homoscedasticity. If the homoscedasticity assumption is violated then it is called heteroscedasticity. The consequences of heteroscedasticity are the estimator remain linear and unbiased, but it can cause estimator haven‘t a minimum variance so the estimator is no longer BLUE. The purpose of this study is to analyze and resolve the violation of heteroscedasticity assumption with Weighted Least Square(WLS) and Quantile Regression. Based on the results of the comparison between WLS and Quantile Regression obtained the most precise method used to overcome heteroscedasticity in this research is the WLS method because it produces that is greater (98%).