What are the steps to solve this question using STATA? Which commands do I have to use?

a) OLS: Estimate the effect of seat belt use on fatalities by regressing FatalityRate onsb_useage, speed65, speed70, ba08, drinkage21, ln(income), and age. Does the estimated OLS regression suggest that increased seat belt use reduces fatalities? Report and interpret the coefficient on sb_useage .

b) Fixed effect regression: Estimate the effect of seat belt use on fatalities by regressing FatalityRate onsb_useage, speed65, speed70, ba08, drinkage21, ln(income), and age with state fixed effect.(Hint: you need to set entity index and time index first in Stata before running the xtreg command, also you need to cluster the regression error at state level to correct for serial autocorrelation over time) Report coefficient on sb_useage. Is the coefficient significantly different from 0? Report the t-statistic and p-value. Do the results change when you add state fixed effects? Provide an intuitive explanation for why the results changed.

c) Time fixed effect regression: do the results change when you add time fixed effects plus state fixed effects? Report coefficient on sb_useage and construct 95% confidence interval for sb_useage. (Hint: you need to add T-1 year dummy variable into the state fixed effect model you have done in part b. Exclude the first year from your regression model. Use the global year dummy year84 year85 year86... year97 will shorten your code and save you time later.)

d) Using the results in (c), discuss the size of the coefficient on sb_useage. Is it large? Small? How many lives would be saved if seat belt use increased from 52% to 90%?

e) There are two ways that mandatory seat belt laws are enforced: Primary enforcement means that a police officer can stop a car and ticket the driver if the officer observes an occupant not wearing a seat belt; secondary enforcement means that a police officer can write a ticket if an occupant is not wearing a seat belt, but must have another reason to stop the car. In the data set, the primary is a binary variable for primary enforcement and secondary is a binary variable for secondary enforcement. Run a regression of sb_useageon primary, secondary, speed65, speed70, ba08, drinkage21, ln(income), and age, including fixed state and time effects in the regression. Does primary enforcement lead to more seat belt use? What about secondary enforcement?Report coefficients on primary and secondary variable and their p-values.