Sift - Mahalanobis Distance Dialog

Mahalanobis Distance is a common measure used to determine outliers in a data sample. The Mahalanobis distance can be conceptualized as the distance from a point to a centroid of a data set, taking into account correlations in the data set. The Mahalanobis distance method can be used on PCA results. This is done by measuring the distance of each point to the centroid in the transformed PCA space.

The Mahalanobis Distance is found on the toolbar and under 'Outlier Detecting Using PCA' in the Analysis menu.

Dialog

The Mahalanobis Distance using PCA allows the user to automatically search the data and identify traces that are outside the norm. The search for outliers can be done by Combined Groups, Group, and Workspaces. Users can decide if they want to auto-exclude any outliers that are detected in the groups or workspaces, and specify any thresholds and P-values of high, low, and combined variability.

• Combined group passes: The number of passes to make on the combined groups.
• Group passes: The number of passes to make on each group.
• Workspace passes: The number of passes to make on each workspace.
• Separate conditions in test: If conditions should be treated as separate groups.
• Auto-exclude results: If outliers should be automatically excluded from the results.
• High variability PC threshold: .
• High variability P-Value: .
• Low variability P-Value: .
• Combined variability P-Value : .

Results

The Mahalanobis Distance results appear upon completion of running the test.