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14-22 Introduction to Multiple Regression One of the Most Common (Y)( Y )

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14-22 Introduction to Multiple Regression 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 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X1)\left( X _ { 1 } \right) and the amount of insulation in inches (X2)\left( X _ { 2 } \right) . Given below is EXCEL output of the regression model.
 Regression Statistics  Multiple R 0.5270 R Square 0.2778 Adjusted R Square 0.1928 Standard Error 40.9107 Observations 20\begin{array}{lr}\hline {\text { Regression Statistics }} \\\hline \text { Multiple R } & 0.5270 \\\text { R Square } & 0.2778 \\\text { Adjusted R Square } & 0.1928 \\\text { Standard Error } & 40.9107 \\\text { Observations } & 20 \\\hline\end{array}

ANOVA
 14-22 Introduction to Multiple Regression 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 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit  \left( X _ { 1 } \right)  and the amount of insulation in inches  \left( X _ { 2 } \right) . Given below is EXCEL output of the regression model.  \begin{array}{lr} \hline {\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.5270 \\ \text { R Square } & 0.2778 \\ \text { Adjusted R Square } & 0.1928 \\ \text { Standard Error } & 40.9107 \\ \text { Observations } & 20 \\ \hline \end{array}   ANOVA     \begin{array}{lrrrrrr}  & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & 448.2925 & 90.7853 & 4.9379 & 0.0001 & 256.7522 & 639.8328 \\ \text { Temperature } & -2.7621 & 1.2371 & -2.2327 & 0.0393 & -5.3721 & -0.1520 \\ \text { Insulation } & -15.9408 & 10.0638 & -1.5840 & 0.1316 & -37.1736 \end{array}   Also  \operatorname { SSR } \left( X _ { 1 } \mid X _ { 2 } \right) = 8343.3572  and  \operatorname { SSR } \left( X _ { 2 } \mid X _ { 1 } \right) = 4199.2672  -Referring to Scenario 14-6, the coefficient of partial determination  r _ { Y 1 \cdot 2 } ^ { 2 }  ⋅ is ____.

 Coefficients  Standard Error t Stat  P-value  Lower 95%  Upper 95%  Intercept 448.292590.78534.93790.0001256.7522639.8328 Temperature 2.76211.23712.23270.03935.37210.1520 Insulation 15.940810.06381.58400.131637.1736\begin{array}{lrrrrrr} & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \\\hline \text { Intercept } & 448.2925 & 90.7853 & 4.9379 & 0.0001 & 256.7522 & 639.8328 \\\text { Temperature } & -2.7621 & 1.2371 & -2.2327 & 0.0393 & -5.3721 & -0.1520 \\\text { Insulation } & -15.9408 & 10.0638 & -1.5840 & 0.1316 & -37.1736\end{array}

Also SSR(X1X2)=8343.3572\operatorname { SSR } \left( X _ { 1 } \mid X _ { 2 } \right) = 8343.3572 and SSR(X2X1)=4199.2672\operatorname { SSR } \left( X _ { 2 } \mid X _ { 1 } \right) = 4199.2672
-Referring to Scenario 14-6, the coefficient of partial determination rY122r _ { Y 1 \cdot 2 } ^ { 2 } ⋅ is ____.


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