Use the data inloanappfor this exercise. the binary variable to be explained is approve, which isequal to one if a mortgage loan to an individual was approved. the key explanatory variable is white, adummy variable equal to one if the applicant was white. the other applicants in thedata set are blackand hispanic. to test for discrimination in the mortgage loan market, a linear probability model can be used: 푎푝푝푟표푣푒=훽0+훽1푤ℎ푖푡푒+표푡ℎ푒푟푓푎푐푡표푟푠a. if there is discrimination against minorities, and the appropriate factors have been controlledfor, what is the sign of 훽1? b. regress approveon whiteand report the results in the usual form. interpret the coefficient onwhite. is it statistically significant? is it practically large? c. as controls, add the variables hrat, obrat, loanprc, unem, male, married, dep, sch, cosign, chist, pubrec, mortlat1, mortlat2, andvr. what happens to the coefficient on white? is there stillevidence of discrimination against nonwhites? d. now, allow the effect of race to interact with the variable measuring other obligations as apercentage of income (obrat). is the interaction term significant? e. using the model from part (d), what is the effect of being whiteon the probability of approvalwhen obrat=32, which is roughly the mean value in the sample?