2. Suppose we decide to include another independent variable into the model abov

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2. Suppose we decide to include another independent variable into the model above to help explain housing starts. (This variable is also included in the Excel data set): mortgage_availability = availability for mortgage money index a. Run the new regression (1 point): housing_starts = 0 + 1*population + 2*mortgage_availability b. Report the coefficient and p-value on population and mortgage_availability. Are they statistically significant? (2 points) c. Using STATA, plot the residuals of this new regression (attach this to the assignment). How do these residuals compare to the original regression’s residuals? (3 points) d. Perform a Durbin-Watson test at the .05 level testing that there is no positive serial correlation. Be sure to report the lower and upper limit critical values and test statistic. What is your decision, and what does this mean? (5 points) e. What does your new decision suggest about the nature of the serial correlation in the first regression? (2 points)

year housing population mortgage_availability 1 0.0909 2.2 0.03635 2 0.08942 2.222 0.03345 3 0.09755 2.244 0.0387 4 0.0955 2.267 0.03745 5 0.09678 2.28 0.04063 6 0.10327 2.289 0.04237 7 0.10513 2.289 0.04715 8 0.1084 2.29 0.04883 9 0.10822 2.299 0.04836 10 0.10741 2.3 0.0516 11 0.10751 2.3 0.04879 12 0.11429 2.34 0.05523 13 0.11048 2.386 0.0477 14 0.11604 2.433 0.05282 15 0.11688 2.482 0.05473 16 0.12044 2.532 0.05531 17 0.12125 2.58 0.05898 18 0.1208 2.605 0.06267 19 0.12368 2.631 0.05462 20 0.12679 2.658 0.05672 21 0.12996 2.684 0.06674 22 0.13445 2.711 0.06451 23 0.13325 2.738 0.06313 24 0.13863 2.766 0.06573 25 0.13964 2.793 0.07229

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2. Suppose we decide to include another independent variable into the model abov

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