Calculate the probability that Mr. A smokes. Do the calculation \"by hand.\" That is, calculate the \"z-value\" of the probit model using the estimated coefcients, then look up the probability in a standard normal table. Show your work. (b) Repeat the calculation in 2(a) but have your software do the calculation for you. (0) Test the hypothesis that the coefficient on smkban is zero in the population version of this probit regression, against the alternative that it is nonzero, at the 2% significance level. Compare your t-statistic and your conclusion with those of question 1(d) based on the linear probability model. ((1) Test the hypothesis that the probability of smoking does not depend on age in this probit model. (e) Test the hypothesis that the probability of smoking does not depend on race in this probit model. Compare (very briey only in words) the Probit and Logit results and explain why these results are different/similar. Read Table 1 below (and the notes to that Table). Then, fill in the missing entries. Compare the Marginal Effects of Smoke Ban (smkban) on the probability of smoking for the two models used. In a single Table (Table 2), show the estimated parameters and the marginal effects of m models. Make sure to include the relevant statistics (for each coefficient). The marginal effects should be at the means of the data. Discuss the similarities and differences. (Note: make sure to calculate the marginal effects of the discrete dummy variables correctly.) Summarize the main results (very briey). In addition, make sure to show the prediction tables for the probit and lo git models. Would you use the linear probability model to do all of the above analyses? Explain