PENERAPAN METODE BOOTSTRAP RESIDUAL DALAM MENGATASI BIAS PADA PENDUGA PARAMETER ANALISIS REGRESI - ASTARI, NI MADE METTA; SUCIPTAWATI, NI LUH PUTU; SUKARSA, I KOMANG GDE (E-Jurnal Matematika; Vol 3 No 4 (2014)DO - 10.24843/MTK.2014.v03.i04.p075, 2014)
Statistical analysis which aims to analyze a linear relationship between the independent variable and the dependent variable is known as regression analysis. To estimate parameters in a regression analysis method commonly used is the Ordinary Least Square (OLS). But the assumption is often violated in the OLS, the assumption of normality due to one outlier. As a result of the presence of outliers is parameter estimators produced by the OLS will be biased. Bootstrap Residual is a bootstrap method that is applied to the residual resampling process. The results showed that the residual bootstrap method is only able to overcome the bias on the number of outliers 5% with 99% confidence intervals. The resulting parameters estimators approach the residual bootstrap values ?? OLS initial allegations were also able to show that the bootstrap is an accurate prediction tool.
Keywords: regression analysis, outlier, biases, bootstrap residuals