Robust estimates of parameters are less sensitive to outliers than non-robust estimates. Robust Fit Outliers provides several types of robust estimates of the center and spread of your data to determine those values that can be considered extreme. Figure 2.7 shows the default Robust Fit Outliers window.
Figure 2.7 Robust Fit Outliers Window
Given a robust estimate of the center and spread, outliers are defined as those values that are K times the robust spread from the robust center. The Robust Fit Outliers window provides several options for calculating the robust estimates and multiplier K as well as provides tools to manage the outliers found.
Uses Huber M-Estimation to estimate center and spread. This option is the default. See Huber and Ronchetti (2009).
The multiplier that determines outliers as K times the spread away from the center. Large values of K provide a more conservative set of outliers than small values. The default is 4.
Sets the Exclude Row state for outliers in the selected columns in the data table. Click Rescan to update the Robust Estimates and Outliers report.
Adds the selected outliers to the missing value codes column property for the selected columns. Use this option to identify known missing value or error codes within the data. Click Rescan to update the Robust Estimates and Outliers report.
Changes the outlier value to a missing value in the data table. Click Rescan to update the Robust Estimates and Outliers report.