TABLE 14-6

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 (), the amount of insulation in inches (), the number of windows in the house (), and the age of the furnace in years ().Given below are the EXCEL outputs of two regression models.

Model 1 Regression Statistics R Square 0.8080 Adjusted R Square 0.7568 494 Multiple Regression Models Observations 20 ANOVA SS MS F Significance F Regression 169503.4241 42375.86 15.7874 2.96869E-05 Residual 15 40262.3259 2684.155 Total 10 209765.75 Coefficients Standard Error 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 X. (Furnace Age) 6.3780 4.1026 1.5546 0.1408 -0.8140 13.5702 + Model 2 Regression Statistics R Square 0.7768 Adjusted R Square 0.7506 Observations 20 ANOVA df Significance SS MS F Regression Residual 2 162958.2277 81479.11 29.5923 2.9036E-06 46807.5222 2753.384 Total 17 19 209765.75 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 489.3227 43.9826 11.1253 3.17E-09 396.5273 582.1180 X1 (Temperature) -5.1103 0.6951 -7.3515 1.13E-06 -6.5769 -3.6437 X2 (Insulation) -14.7195 4.8864 -3.0123 0.0078 -25.0290 -4.4099