Finding Outliers in Regression Data Abstract: Robust regression algorithms are effective at reducing the effects of outliers in multivariate regression. M-estimators in particular select an influence function, such as Tukey's biweight, to downweight large residuals. Determining the proper value of robust scale in the influence function is critical to successful application. Alternatively, the influence function can be specified indirectly by (1) making a specific assumption of the parametric form of the residuals (normal with zero mean, for example) and (2) using a minimum-distance fitting criterion in place of least-squares. We examine the "natural" influence functions that result and examine several regression problems with outliers. Key Words: Minimum distance estimation M-estimation Influence Function Integrated square error