Using the campus data set, model factors associated with college crime across States. a. Estimate and report OLS parameter estimates for the equation. Present your results in a comparison table. Include standard errors and t-statistics in a separate column. Indicate which coefficients are statistically significant. b. Plot your fitted crime values separately against private school, police, and enrollment. c. Plot the fitted residuals (errors) separately against private school, police, and enrollment. What do you notice? Do the errors visually exhibit homoscedasticity or hetroskedasticity? d. Complete this question by hand. Implement a White test with linear, squared, and interactive regressors for homoskedastic error terms. What are the hypothesis, test statistic, critical value, and decision? e. If these are hetroskedastic errors, what are the consequences on the model standard errors and t-statistics? f. Take the natural log of crime and regress it on the same variables. Present your results as a comparison table next to your first OLS estimates. Include t-statistics in a separate column. Indicate which coefficients are statistically significant. g. Estimate the same model with White or robust standard errors. Report coefficients, standard errors, and t-statistics in the same table with the original, natural log, and robust estimates