Free - Response #2. To test whether a mortgage applicant's payment to income ratio affects if a mortgage application is denied , Tobin set up the following regression equation with a binary dependent variable : deny ; = Bo + B, x ?i_ra?i? +?;, with the following definitions of independent and dependent variables Variable Description deny = 1if mortgage application is denied pi_ratio anticipated monthly loan payments / monthly income Tobin obtained the following regression results: Linear regression Number of obs - 2380 F( 1, 2378) - 37 .56 Prob > F 0 . 0000 R-squared 0. 0397 Root MSE . 31828 Robust deny Coef. Std. Err. t P> ItI (95% Conf. Interval] pi ratio . 6035349 0984826 6.13 0. 000 . 4104144 . 7966555 cons - . 0799096 . 0319666 -2.50 0 . 012 - . 1425949 - . 0172243 (a) (3 points ) Interpret the regression results for the pi_ratio variable . What is the model's prediction for an applicant with a pi_ratio = 1? (b) (3 points) What is the model's prediction for an applicant with a pi_ratio = 2? Discuss any potential issues with this result (c) (3 points ) Tobin re-estimated the relationship using a Probit regression and obtained the below results . Interpret the regression results for the pi_ratio variable . Calculate the change in probability of an increase in ?i_ra?i? from 0.1 to 0.2. Probit regression Number of obs 2380 LR chi2( 1) 80.59 Prob > chi2 0 . 0000 Log likelihood - -831. 79234 Pseudo R2 0. 0462 deny Coel. Sta. Err. 2 P>|21 (958 Conf. Interval] pi ratio 2. 967907 3591054 8. 26 0. 000 2.264073 3. 67174 cons -2. 194159 . 12899 -17 .01 0 . 000 2.446974 -1. 941343 (d) (3 points) When is it most appropriate to use a Probit model as opposed to traditional OLS multiple regression