The manager in charge of deciding the credit policy to clients (they are firms) gets info on new clients from a

public database collecting their past behavior. Let X be a score summarizing the information recovered from

this database. It takes value on a range from 1-'highly unreliable client' to 10-'higly reliable client'.

In addition, the manager maintains her own database on behavior of her current/past clients. Let Y be the

delay (in days) of her clients on the date established to pay back their debt. Regressing Y on X she gets a

model to predict future - not yet observed - behavior Y of a new client based on X:

Y = 62.2 - 5.3*X + e, R2 = 0.52, var{e} = 16.8,

(1.4)

standard error for the estimated coefficient in parentheses.

Q1. Would you say that the score X is a good predictor of Y? Clarify.

Q2. Derive the value of R2. Considering the available elements, would you add S to the model? Explain.

Q3. Would you say that the incentive works? Explain.

Q4. What does the coefficient on X*D mean?