@article{mtk, author = {NI MADE ASTARI and NI LUH PUTU SUCIPTAWATI and I KOMANG SUKARSA}, title = { PENERAPAN METODE BOOTSTRAP RESIDUAL DALAM MENGATASI BIAS PADA PENDUGA PARAMETER ANALISIS REGRESI}, journal = {E-Jurnal Matematika}, volume = {3}, number = {4}, year = {2014}, keywords = {regression analysis; outlier; biases; bootstrap residuals}, abstract = {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.}, pages = {130--137}, doi = {10.24843/MTK.2014.v03.i04.p075}, url = {https://ojs.unud.ac.id/index.php/mtk/article/view/11994} }