One of the most common questions of prospective house buyers pertains to the average cost of heating in dollars (Y). To provide its customers with information on that matter, a large real estate firm used the following 4 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X1), the amount of insulation in inches (X2), the number of windows in the house (X3), and the age of the furnace in years (X4). Given below are the outputs of two regression models.

MODEL: 2

What can we say about Model 1 and Model 2? Which models do you prefer?

Transcribed Image Text:

Regression Statistics R Square 0.8080 Adjusted R Square 0.7568 Observations 20 ANOVA Significance F df SS MS 42375.86 Regression 169503.4241 15.7874 2.96869E-05 Residual 15 40262.3259 2684.155 Total 19 209765.75 Coefficients Standard Error t Stat P-value Lower 90.0% Upper 90.0% Intercept 421.4277 77.8614 5.4125 7.2E-05 284.9327 557.9227 X1 (Temperature) -4.5098 0.8129 -5.5476 5.58E-05 -5.9349 -3.0847 X2 (Insulation) -14.9029 5.0508 -2.9505 0.0099 -23.7573 -6.0485 X3 (Windows) 0.2151 4.8675 0.0442 0.9653 -8.3181 8.7484 X4 (Furnace Age) 6.3780 4.1026 1.5546 0.1408 -0.8140 13.5702