Computer problems (show any relevant Stata output): An adaptation of Wooldridge C2.6 (note that log(expend) is in the data as the lexpend variable): Use the data MEAP93.DTA to explore the relationship between the graduation rate (gradrate) and spending per student (expend). (i) Do you think each dollar spent has the same effect on the graduation rate, or does a diminishing effect seem more appropriate? (ii) In the population model gradrate = ?0 + ?1 log(expend) + u, argue that ?1/10 is the percentage point change in gradrate given a 10% increase in expend. (iii) Use the data to estimate the model from part (ii). Report the estimated equation in the usual way, including the sample size and R-squared. (iv) How big is the estimated spending effect? Specifically, if spending increases by 10%, what is the estimated percentage point increase in gradrate? (v) One might worry that regression analysis can produce fitted values for gradrate that are greater than 100. Why is that not much of a worry in this data set? (vi) Plot gradrate versus lexpend with the fitted regression line shown. (vii) To visualize the non-linearity described by this model, create the fitted values from the regression and then do a scatter plot of the fitted values versus expend (not lexpend). Explain how the effect of expenditures changes at higher values of expenditures. (viii) Suppose that we wanted to measure gradrate as a fraction (a number between 0 and 1) rather than on a 0-to-100 scale. Specifically, we could do the following in Stata to re-scale the math10 variable: . replace gradrate = gradrate / 100 (This replaces the original gradrate values with the values divided by 100.) If you re-ran the regression (now regressing the re-scaled gradrate upon lexpend), how would the following quantities change (compared to the original results)? Be specific, and try these on your own before checking your answers in Stata. (a) R-squared (b) SST (c) the slope estimate (d) the intercept estimate in Homework Help