PROC DISTANCE with METHOD=MAHALANOBIS requires an additional option to compute Mahalanobis distance. What does Mahalanobis distance account for that Euclidean distance does not?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — you want Mahalanobis distance because it uses the covariance matrix to account for correlations and scale. That keeps correlated variables from double-counting variation and makes the distance scale-invariant.
Full Explanation
The correct answer is the covariance-matrix option: Mahalanobis distance weights differences by the inverse covariance, so it accounts for correlations and scale, preventing correlated variables from double-counting variation and making the distance scale-invariant. For uncorrelated variables with equal variance, it reduces to Euclidean distance. The categorical-variable option is wrong because Mahalanobis is for continuous variables only. The bounded-between-0-and-1 option is wrong because it is not bounded. The negative-values option is wrong because both distance metrics handle negative values.