This demonstration illustrates why multivariate outliers might not be apparent
in univariate views, but become readily apparent on the *smallest* principal
component.

For bivariate data, principal component scores are equivalent to a rotation of the data to a view whose coordinate axes are aligned with the major-minor axes of the data ellipse. It is shown here by linear iterpolation between the original data, XY, and PCA scores, of the form

xy <- XY + α * (PCA-XY)where α ranges from 0 to 1.

The data shown here were genereated as 100 obsservations on two correlated normal variables with two bivariate outliers near (2,2), (-2, -2).