Unpacking Outlier with Weight Least Square (Implemented on Pepper Plantations Data) - Prasetya, Rizka Pradita (Parameter: Journal of Statistics, 2022)
Outliers in regression analysis can cause large residuals, the diversity of the data becomes greater, causing the data to be heterogenous. If an outlier is caused by an error in recording observations or an error in preparing equipment, the outlier can be ignored or discarded before data analysis is carried out. However, if outliers exist not because of the researcher's error, but are indeed information that cannot be provided by other data, then the outlier data cannot be ignored and must be included in data analysis. There are several methods to deal with outliers. The Weight Least Square method produces good results and is quite resistive to outliers. The WLS method is used to overcome the regression model with non-constant error variance, because WLS has the ability to neutralize the consequences of violating the normality assumption caused by the presence of outliers and can eliminate the nature of unusualness and consistency of the OLS estimate. To compare the level of estimator accuracy between regression models, the mean absolute percentage error (MAPE) is used. Based on the results of this study, it was concluded that the WLS method produced a smaller Mean Absolute Percentage Error value so that the use of this method was more appropriate because it was not susceptible to the effect of outliers.