Multivariate Robust Outliers

The Multivariate Robust Fit Outliers tool uses the Robust option in the Multivariate platform to examine the relationships between multiple variables. For more information about how the Multivariate platform works, see Correlations and Multivariate Techniquesin the Multivariate Methods book.

Outlier Analysis

The Outlier Analysis calculates the Mahalanobis distances from each point to the center of the multivariate normal distribution. This measure relates to contours of the multivariate normal density with respect to the correlation structure. The greater the distance from the center, the higher the probability that it is an outlier. For more information about the Mahalanobis distance and other distance measures, see Multivariate Platform Options in the Multivariate Methods book.

After the rows are excluded, you are given the option to either rerun the analysis or close the utility. Rerunning the analysis recalculates the center of the multivariate distribution without those excluded rows. Note that unless you hide the excluded rows in the data table, they still appear in the graph.

You can save the distances to the data table by selecting the Save option from the Mahalanobis Distances red triangle menu.

Figure 2.8 Multivariate Robust Outliers Mahalanobis Distance Plot

Figure 2.8 shows the Mahalanobis distances of 16 different columns. The plot contains an upper control limit (UCL) of 4.82.This UCL is meant to be a helpful guide to show where potential outliers might be. However, you should use your own discretion to determine which values are outliers. For more details about this upper control limit (UCL), see Mason and Young (2002).

Multivariate with Robust Estimates Options

The red triangle menu for Multivariate with Robust Estimates contains numerous options to analyze your multivariate data. For a list and description of these options, see Multivariate Platform Options in the Multivariate Methods book.