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

question 45

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SCENARIO 17-2 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y) ( 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) \left( X _ { 1 } \right) , the amount of insulation in inches (X2) \left( X _ { 2 } \right) , the number of windows in the house (X3) \left( X _ { 3 } \right) , and the age of the furnace in years (X4) \left( X _ { 4 } \right) . Given below are the EXCEL outputs of two regression models.

Model 1
 Regression Statistics  R Square 0.8080 Adjusted R Square 0.7568 Observations 20\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}  ANOVA \text { ANOVA }
df SS MSF Significance F Regression 4169503.424142375.8615.78740.0000 Residual 1540262.32592684.155 Total 19209765.75\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}

 Coefficients  Standard Error  t Stat  P-value  Lower 90.0%  Upper 90.0%  Intereept 421.427777.86145.41250.0000284.9327557.9227X1 (Temperature)  4.50980.81295.54760.00005.93493.0847X2 (Insulation)  14.90295.05082.95050.009923.75736.0485X3 (Windows)  0.21514.86750.04420.96538.31818.7484X4 (Furnace Age)  6.37804.10261.55460.14080.814013.5702\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}

 Model 2\text { Model } 2
 Regression Statistics  R Square 0.7768 Adjusted R Square 0.7506 Observations 20\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}

 ANOVA \text { ANOVA }
 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 are the degrees of freedom of the partial F test for  H _ { 0 } : \beta _ { 3 } = \beta _ { 4 } = 0 \quad \text { vs. } H _ { 1 } : \text { At least one } \beta _ { \mathrm { j } } \neq 0 , j = 3,4  ? A)  2 numerator degrees of freedom and 15 denominator degrees of freedom B)  15 numerator degrees of freedom and 2 denominator degrees of freedom C)  2 numerator degrees of freedom and 17 denominator degrees of freedom D)  17 numerator degrees of freedom and 2 denominator degrees of freedom

 Coefficients  Standard Error t Stat  P-value  Lower 95%  Upper 95%  Intercept 489.322743.982611.12530.0000396.5273582.1180X1 (Temperature)  5.11030.69517.35150.00006.57693.6437X2 (Insulation)  14.71954.88643.01230.007825.02904.4099\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 are the degrees of freedom of the partial F test for H0:β3=β4=0 vs. H1: At least one βj0,j=3,4H _ { 0 } : \beta _ { 3 } = \beta _ { 4 } = 0 \quad \text { vs. } H _ { 1 } : \text { At least one } \beta _ { \mathrm { j } } \neq 0 , j = 3,4 ?


Definitions:

McKinley's Victory

Refers to the election win of William McKinley in the 1896 United States presidential election, which was significant for its emphasis on economic issues.

Pullman Strike

A nationwide railroad strike in the United States in 1894, triggered by wage reductions at the Pullman Company and leading to a significant labor conflict.

National Rail Service

A network of trains operated by one or more companies, providing nationwide train transportation in a specific country.

Eugene V. Debs

An American socialist, political activist, and trade unionist, known for his role in founding the Industrial Workers of the World and for running unsuccessfully for U.S. president five times as a candidate of the Socialist Party.

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