Answer Happy

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 following Excel output shows the partially completed regression output from a random sample of homes that have recently sold. What is the critical value to test the significance of the regression coefficients using a=0.057 Round to three decimal places. Click here to view the regression summary output. Click here to view the confidence interval estimate Gm A. 2.145 OB. 2.179 OC. 2.201 OD. 2.131
Regression summary output SUMMARY OUTPUT Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Age Living Area Bedrooms Regression Statistics 0.8486 36,009.01 SS 36,709,265,905.70 14 50,972,400,000.00 Standard Error 101,922.3333 2,092.4981 27.6994 11,006.8696 Coefficients 108,597.3721 -580.6870 86.8282 31,261.9127 MS t Stat F P-value 0.3095 0.7865 0.0095 0.0161 Significance F 0.0022 Lower 95% Upper 95%
Confidence interval estimate Confidence Interval Estimate and Prediction Interval Data Confidence Level 95% 1 Age given value Living Area given value Bedrooms given value Predicted Y (YHat) Interval Half Width Confidence Interval Lower Limit Confidence Interval Upper Limit Interval Half Width Prediction Interval Lower Limit Prediction Interval Upper Limit For Average Predicted Y (YHat) For Individual Response Y 10 2400 4 436,226 33,577 402,649 469,803 86,074 350,152 522,300