Use types of robust regression in treatment of the outliers in simple linear regression

Abstract

In the analysis of the simple linear regression there is only one independent variable . My be there exist a problem because there are extreme points having higher remains (Residuals) in comparison with those of observations , for there are odd values (outliers ) in the groups of the observations . Usually least square method are used so as to estimation the parameters of a model .The analysis of this regressions begins with data designs of those which are remains in the opposite of an independent variety ; also , in the opposite of estimated value of Y to investigation of assumptions of that model so , the robust regression analysis in place of the least square method with outliers . This research deals with four types of assessment applied to one example along with the use of the criterions ( AIC and BIC ) applied through goodness fit . I used statistical program ( SAS 9.1) in analyzing the results.