ND outliers (multidimensional)

Flags multivariate outliers across several columns at once — unusual combinations that per-column checks miss. (A point can sit within the normal range on each variable on its own, yet be an outlier for the set because it breaks their typical relationship.)

• Select two or more columns in the single Columns field. With only one column selected the step flags an error.

• A row is flagged when its point lies too far from the joint centre, measured by the Mahalanobis distance (which accounts for the correlations between the columns). The cutoff is the chi-square value at 95% confidence, with degrees of freedom equal to the number of columns.

Flagged rows appear as checkboxes under Remove: (all ticked); untick any to keep that respondent, and previewing the data shows the removed rows in red. A few points with non-degenerate spread are needed to estimate the covariance. The step is also toggleable.

To judge outliers one column at a time, use Outliers; within subgroups, use Outliers within groups.