The variable smokes is a binary variable equal to one if a person smokes, and zero otherwise. Using the data in SMOKE. RAW, we estimate a linear probability model for smokes:s?mokes = .656 - .069log(cigpric) + .012log(income) - .029educ(.855) (.204) (.026) (.006)[.856] [.207] [.026] [.006]+.020age - .00026age2 - .101restaurn - .026white(.006) (.00006) (.039) (.052)[.005] [.00006] [.038] [.050]n = 807,R2 = .062.The variable white equals one if the respondent is white, and zero otherwise; the otherindependent variables are defined in Example 8.7, wherecigs = number of cigarettes smoked per day. income = annual income. cigpric = the per pack price of cigarettes (in cents).educ = years of schooling. age = measured in years. restaurn = a binary indicator equal to unity if the person resides in a state with restaurant smokingrestrictions. Both the usual and heteroskedasticity-robust standard errors are reported.(i) Are there any important differences between the two sets of standard errors?(ii) Holding other factors fixed, if education increases by four years, what happens to theestimated probability of smoking?(iii) At what point does another year of age reduce the probability of smoking?(iv) Interpret the coefficient on the binary variable restaurn (a dummy variable equal to one ifthe person lives in a state with restaurant smoking restrictions).(v) Person number 206 in the data set has the following characteristics: cigpric = 67.44,income = 6,500, educ = 16, age = 77, restaurn = 0, white = 0, and smokes = 0. Compute thepredicted probability of smoking for this person and comment on the result.