How would one use Fishers linear classification functions to classify a potential loan candidate as an acceptor or non-acceptor?

A. Calculate the classification scores for both accepting and not accepting and classify them as belonging to the class with the lowest score.

B. Calculate the classification scores for both accepting and not accepting and classify them as belonging to the class with the highest score.

C. Calculate the Euclidean distance to each class and classify it as belonging to the class with the shortest distance.

D. Calculate the Euclidean distance to each class and classify it as belonging to the class with the longest distance.

E. Use domain knowledge to assign the observation to the class you believe the observation will fit best into.

Which of the following is NOT true about statistical distance in discriminant analysis?

A. The Euclidean distance rule ignores correlations between variables.

B. The Euclidean distance rule can classify an observation with multiple predictor values.

C. The Euclidean rule relies upon similar units of measurement for it to be effective in measuring distance.

D. Statistical distance overcomes the drawbacks of the Euclidean rule but can handle only one predictor value.