a) Write the estimated regression equation. (SHOW WORK; 0.25 pt)b) What percent of the total variation in PRICE does the regression equation, or SQFT, explain or determine? (0.25 point)c) What is the value of the bivariate correlation coefficient between PRICE and SQFT in the sample? (0.25 point)d) Interpret the value for the Standard Error under Regression Statistics in this context (please be specific). (0.25 point)e) Interpret the value for the estimated slope coefficient for SQFT (i.e., Size) in this situation or context (please be specific) (0.25 point)f) Interpret the value for the estimated intercept coefficient in this situation. Should we give any importance to the intercept interpretation? (0.5 point)g) State the hypotheses for assessing the statistical significance of the overall regression equation. (0.25 point)h) Does the model overall fit the data? Use (? = 0.05) (0.25 point) YES NO (CIRCLE one)i) State the null and the alternative hypotheses to test whether the slope coefficient for SQFT is significantly greater than zero. (SHOW WORK; 0.5 pt)j) If ? = 0.01, determine the critical value for the test statistic associated with the hypotheses in Part i). (Hint: Note that df = n-p-1) (0.25 point)k) Given the MS Excel output and Part c), state your decision regarding the null hypotheses. (SHOW WORK; 0.25 pt)l) You are a realtor in Seattle. On average, do smaller single-family homes have significantly LOWER or HIGHER selling prices than larger single-family homes? (0.25 point)m) If the SQFT for house A equaled 1250 ft2 and the SQFT for the house B equaled 1550 ft2, by how much (in $s) do you expect the prices of the two houses to differ? (Show work; 0.25 point)

4. The data file seattle_houses contains observations for 497 family home sales in Seattle, from March 10th, 2017 to April 8th, 2017 (source Redfin.com). Use the MS Excel output below, based on a simple regression analysis with PRICE (selling price, in $) as the response variable and SQFT (number of [livable] square feet in the house) as the explanatory variable, to answer/ complete Parts a) through m) SUMMARY OUTPUT - PRICE = f(SIZE in SQUARE FEET) Regression Statistics Multiple R 0.6979 R Square 0.4870 Adjusted R Square 0.4860 Standard Error 227037 Observations 497 ANOVA LL Significance F Regression 2.4226E+13 2.4226E+13 469.9918 9.0138E-74 Residual 495 2.5515E+13 51546018402 Total 496 4.9741E+13 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% -2280 26922.6003 -0.0847 0.9325 -55177 50616 Intercept 263 12.1355 21.6793 9.0138E-74 239 287 SOFT