Estimasi Parameter Regresi Linear Menggunakan Regresi Kuantil - Baiq Devi Rachmawati; Qurratul Aini (Eigen Mathematics Journal, 2018)
Regression analysis is a statistical analysis method for estimating the relationship between dependent variables (Y) and one or more independent variables (X) . As the purpose of this study is to theoretically examine the quantile regression method in estimating linear regression parameters. In regression analysis usually the method used to estimate parameters is the least square method with assumptions that must be met that normal assumption, homoskedasticity, no autocorrelation and non multicollinearity. Basically the least square method is sensitive to the assumptions of deviations in the data, so that the estimations results will be lees good if the assumptions are not fulfilled. Therefore, to overcome the limitations of the least square method developed a quantile regression method for estimating linear regression parameters. Based on the result of research that has been done shows that the estimation of linear regression parameters using the quantile regression method is obtained by minimazing the absolute number of errors through the simplex algorithm.