SCENARIO 17-2 One of the Most Common Questions of Prospective $( Y )$

SCENARIO 17-2 One of the most common questions of prospective house buyers pertains to the 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 $\left( X _ { 1 } \right)$ , the amount of insulation in inches $\left( X _ { 2 } \right)$ , the number of windows in the house $\left( X _ { 3 } \right)$ , and the age of the furnace in years $\left( X _ { 4 } \right)$ . Given below are the EXCEL outputs of two regression models.

Model 1
$\begin{array}{lr}\hline{\text { Regression Statistics }} \\\hline \text { R Square } & 0.8080 \\\text { Adjusted R Square } & 0.7568 \\\text { Observations } & 20 \\\hline\end{array}$ $\text { ANOVA }$
$\begin{array}{lrrrrrr}\hline & d f & & {\text { SS }} & M S & F & \text { Significance } F \\\hline \text { Regression } && 4 & 169503.4241 & 42375.86 & 15.7874 & 0.0000 \\\text { Residual } && 15 & 40262.3259 & 2684.155 & & \\\text { Total } && 19 & 209765.75 & & & \\\hline\end{array}$

$\begin{array}{lrrrrrrr}\hline & \text { Coefficients } & \text { Standard Error } &{\text { t Stat }} & \text { P-value } & \text { Lower 90.0\% } & \text { Upper 90.0\% } \\\hline \text { Intereept } & 421.4277 & 77.8614 & 5.4125 & 0.0000 & 284.9327 & 557.9227 \\\mathrm{X}_{1} \text { (Temperature) } & -4.5098 & 0.8129 & -5.5476 & 0.0000 & -5.9349 & -3.0847 \\\mathrm{X}_{2} \text { (Insulation) } & -14.9029 & 5.0508 & -2.9505 & 0.0099 & -23.7573 & -6.0485 \\\mathrm{X}_{3} \text { (Windows) } & 0.2151 & 4.8675 & 0.0442 & 0.9653 & -8.3181 & 8.7484 \\\mathrm{X}_{4} \text { (Furnace Age) } & 6.3780 & 4.1026 & 1.5546 & 0.1408 & -0.8140 & 13.5702\end{array}$

$\text { Model } 2$
$\begin{array}{lr}\hline {\text { Regression Statistics }} \\\hline \text { R Square } & 0.7768 \\\text { Adjusted R Square } & 0.7506 \\\text { Observations } & 20 \\\hline\end{array}$

$\text { ANOVA }$


$\begin{array}{lrrllrr}\hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \\\hline \text { Intercept } & 489.3227 & 43.9826 & 11.1253 & 0.0000 & 396.5273 & 582.1180 \\\mathrm{X}_{1} \text { (Temperature) } & -5.1103 & 0.6951 & -7.3515 & 0.0000 & -6.5769 & -3.6437 \\\mathrm{X}_{2} \text { (Insulation) } & -14.7195 & 4.8864 & -3.0123 & 0.0078 & -25.0290 & -4.4099\end{array}$
-Referring to Scenario 17-2, what can we say about Model 1?

A) The model explains 77.7% of the sample variability of heating costs; after correcting for the degrees of freedom, the model explains 75.1% of the sample variability of heating
Costs.
B) The model explains 75.1% of the sample variability of heating costs; after correcting for the degrees of freedom, the model explains 77.7% of the sample variability of heating
Costs.
C) The model explains 80.8% of the sample variability of heating costs; after correcting for the degrees of freedom, the model explains 75.7% of the sample variability of heating
Costs.
D) The model explains 75.7% of the sample variability of heating costs; after correcting for the degrees of freedom, the model explains 80.8% of the sample variability of heating
Costs.