The next question(s) refer(s) to the problem setup below. A real estate agent wants to use a multiple regression model to predict the selling price of a home in thousands of dollars) using the following four x variables. Age: Bath: LotArea: TotRms_AbvGrd: age of the home in years total number of bathrooms total square footage of the lot on which the house is built total number of rooms (not counting bathrooms) in the house The agent runs the regression using Excel and gets the following output. Some of the numbers have been replaced by "?" You might not need to use these numbers, depending on the question. Regression Statistics Multiple R 0.749274972 R Square 0.561412984 Adjusted R Square Standard Error 52.94184546 Observations 2930 ANOVA df F Significance F Regression Residual Total SS MS 4 10494233.03 2623558 29258198304.078 2802.839 292918692537.11 Lower 95% Upper 95% 42.55331078 61.63528878 -1.108170258 -0.956543776 Intercept Age Bath LotArea TotRms_AbvGrd Coefficients 52.09429978 -1.032357017 20.75090863 0.001511222 15.5802549 Standard Errort Stat P-value 4.865926641 10.70594 2.91E-26 0.03866493 -26.7001 9.7E-141 1.396538039 14.85882 3.34E-48 0.00012776 11.82861 1.44E-31 0.718315975 ? 7.38E-97 0.001260714 14.17179864 0.001761731 16.98871115 Write out the fitted model by filling in the blanks below. Round all numbers to three (3) decimal places. price = (Age) + + ) + (TotRms_AbvGrd) The next question(s) refer(s) to the problem setup below. A real estate agent wants to use a multiple regression model to predict the selling price of a home in thousands of dollars) using the following four x variables. Age: Bath: LotArea: TotRms_AbvGrd: age of the home in years total number of bathrooms total square footage of the lot on which the house is built total number of rooms (not counting bathrooms) in the house The agent runs the regression using Excel and gets the following output. Some of the numbers have been replaced by "?" You might not need to use these numbers, depending on the question. Regression Statistics Multiple R 0.749274972 R Square 0.561412984 Adjusted R Square Standard Error 52.94184546 Observations 2930 ANOVA df F Significance F Regression Residual Total SS MS 4 10494233.03 2623558 29258198304.078 2802.839 292918692537.11 Lower 95% Upper 95% 42.55331078 61.63528878 -1.108170258 -0.956543776 Intercept Age Bath LotArea TotRms_AbvGrd Coefficients 52.09429978 -1.032357017 20.75090863 0.001511222 15.5802549 Standard Errort Stat P-value 4.865926641 10.70594 2.91E-26 0.03866493 -26.7001 9.7E-141 1.396538039 14.85882 3.34E-48 0.00012776 11.82861 1.44E-31 0.718315975 ? 7.38E-97 0.001260714 14.17179864 0.001761731 16.98871115 Write out the fitted model by filling in the blanks below. Round all numbers to three (3) decimal places. price = (Age) + + ) + (TotRms_AbvGrd)
