Comparison of OLS Regression and Robust Regression in Overcoming Outlier Problems (Case Study: Cost of Living Data for Urban Areas in Indonesia) - Susiana, Susiana; Chairunisah, Chairunisah; Refisis, Nice Rejoice (ZERO: Jurnal Sains, Matematika dan Terapan, 2024)
Multiple regression analysis in quantitative statistical studies describes the relationship between independent and dependent variables. On the other hand, outliers in a set of data can have an unfavourable influence on data analysis, such as high residuals, significant variances, and bias, and can even cause errors in decision-making. It can be done in several ways to overcome the outlier problem in multiple linear regression analysis, including using robust regression or Ordinary Least Square (OLS) Regression by removing data indicated as an outlier first. The OLS Regression method forms a regression model by minimizing the sum of squared residuals from the estimator of the regression equation. Meanwhile, robust regression is closer to the average parameters and variance-covariance of a particular estimator, namely by standardizing the estimator for the average parameters and variance-covariance in such a way as to produce a consistent estimator for these parameters. This research aims to compare the OLS Regression and robust regression methods as alternatives for dealing with outlier problems in data. The data used in this research is secondary data (cost of living) from the Cost of Living Survey conducted by The Central Statistics Agency of the Republic of  Indonesia in 2018. The stages of this research method are literature study, data collection, descriptive analysis to see the characteristics of the data, forming a regression model using the OLS Regression method, testing classical assumptions, creating a new regression model OLS Regression, forming a regression model with Robust Regression, calculating the MSE (Mean Square Error) of each regression model formed, determining the best regression model, The results of the research show that for the cost of living data, the best regression model is obtained through the OLS Regression method with data without outliers, namely .