Contour map drawn from a two‐dimensional (2‐D) array of sums of squares and cross products supposed to represent an estimated, nonseparable variance–covariance matrix.

90% prediction regions constructed from unimodal regression (pink area in the left panel) and multimodal regression (light blue area in the right panel). Clearly, the prediction region is much smaller in the multimodal regression than in unimodal regression because multimodal regression detects all components whereas unimodal regression discovers only the main component.

What is the most important predictor? Easy to assess if predictors are unrelated (left). Not so easy to assess if predictors are related (right).