Q1. Scenario: Below is a multiple regression in which the dependent variable is market value of houses and the independent variables are the age of the house and square footage of the house. The regression was estimated for 42 houses. (Hint: Fill missing values first). SUMMARY OUTPUT Regression Statistics Correlation coefficient 0.745495 R Square ? Adjusted R Square ? Standard Error 7211.848 Observations 42 Coefficients Standard t Stat P-value Lower 95% Upper 95% Error Intercept 47331.38 13884.34664 3.408974 0.001528 19247.6673 75415.0958 House Age -825.161 607.3128421 ? 0.182046 -2053.5662 403.243744 Square Feet 40.91107 6.696523994 ? 3.65E-07 27.3660835 54.4560534 A. What is the estimated regression equation for determining the market value of houses? B. If the age of a house is 25 years with 1,500 square feet, what is the estimated market value of the house? C. What percentage of the variation in the dependent variable, Market Value, is explained by the regression model? D. If the age of a house increases by 1 year given that the square feet is held constant, what is the impact on the house's market value? E. By examining the t-statistics associated with the regression coefficients, at the 5 percent significance level, which of the two independent variables are statistically different from zero? F. Based on the 95 percent confidence intervals for each of the partial regression coefficients, which independent variable is statistically different from zero and why?