1. 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

?=?0.05?

Round to three decimal places.

- X Regression summary output SUMMARY OUTPUT Regression Statistics Multiple R 0.8486 R Square Adjusted R Square Standard Error 36,009.01 Observations ANOVA df SS MS F Significance F Regression 36,709,265,905.70 0.0022 Residual Total 14 50,972,400,000.00 Coefficients Standard Error t Stat P-value Lower 95% Upper 95. Intercept 108,597.3721 101,922.3333 0.3095 Age -580.6870 2,092.4981 0.7865 Living Area 86.8282 27.6994 0.0095 Bedrooms 31,261.9127 11,006.8696 0.0161X Confidence interval estimate Confidence Interval Estimate and Prediction Interval Data Confidence Level 95% Age given value 10 Living Area given value 2400 Bedrooms given value Predicted Y (YHat) 436,226 For Average Predicted Y (YHat) Interval Half Width 33,577 Confidence Interval Lower Limit 402,649 Confidence Interval Upper Limit 469,803 For Individual Response Y Interval Half Width 86,074 Prediction Interval Lower Limit 350,152 Prediction Interval Upper Limit $22,300