A home appraisal company would like to develop a regression model that would predict the selling price of a house based on the age of the house in years (Age), the living area of the house in square feet (Living Area) and the number of bedrooms (Bedrooms). The given Excel output shows the partially completed regression output from a random sample of homes that have recently sold. Which group of independent variables is significant in this model using a = 0.05? Click the icon to view the Excel output. Excel Output X O A. Living Area, Bedrooms O B. Age, Living Area, Bedrooms O C. Age, Bedrooms SUMMARY OUTPUT Confidence Interval Estimate and Prediction Interval O D. Age, Living Area Regression Statistics Data Multiple R 0.8486 Confidence Level 958 R Square Adjusted R Square Age given value 10 Standard Error 36,009.01 Living Area given value 2400 Observations Bedrooms given value ANOVA Predicted Y (YHat) 436,226 of SS MS F Significance F Regression 36,709,265,905.70 0.0022 For Average Predicted Y (YHat] Residual Interval Half Width 33,577 Total 14 50,972,400,000.00 Confidence Interval Lower Limit 402.649 Confidence Interval Upper Limit 469,803 Coefficients Standard Error + Stat P-value Lower 95 Upper 95% For Individual Response Y Intercept 108,597.3721 101,922.3333 0.3095 Interval Half Width 86,074 Age -580.6870 2,092.4981 0.7865 Prediction Interval Lower Limit 350,152 Living Area 86.8282 27.6994 0.0095 Prediction Interval Upper Limit 522,300 Bedrooms 31,261.9127 11,006.8696 0.0161A car manufacturer would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on the employee's number of years of sales experience (Exp), the employee's weekly base salary before commissions (Salary), and the education level of the employee. There are three levels of education in the sales force-high school degree, associate's degree, and bachelor's degree. The following dummy variables have been defined: ED1 = 1 for a Bachelor's degree, ED1 =0 otherwise; ED2 = 1 for an Associate's degree, ED2 =0 otherwise. The given Excel output shows the partially completed regression output from a random sample of employees from the previous month. According to the analysis, what is the predicted number of cars sold in a month for an employee with seven years of sales experience, a base salary of $400, and a bachelor's degree? Click the icon to view the Excel output. i Excel Output X O A. 13.3 O B. 11.4 O C. 12.1 SUMMARY OUTPUT Confidence Interval Estimate and Prediction Interval O D. 12.7 Regression Statistics Data Multiple R 0.9035 Confidence Level 95% R Square Exp given value Adjusted R Square Salary given value 400 Standard Error 1.7353 ED1 given value Observations ED2 given value ANOVA Predicted Y [YHat) 11.37451 MS F Significance F Regression 4.01126-07 For Average Predicted Y (YHat) Residual 60.23 Interval Half Width 1.867459 9.507054 Total 24 327.84 Confidence Interval Lower Limit Confidence Interval Upper Limit 13.24197 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% For Individual Response Y Intercept 1.6448 1.6352 0.3265 Interval Half Width 4.073127 Exp 0.2393 0.1254 0.0709 Prediction Interval Lower Limit 7.301387 Salary 0.0127 0.0044 0.0097 Prediction Interval Upper Limit 15.44764 ED1 2.9845 1.3553 0.0396 ED2 2.5638 1.0705 0.0265
