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A Local Parent Group Was Concerned with the Increasing Cost

Question 56

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A local parent group was concerned with the increasing cost of school for families with school aged children.The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year.They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables.The multiple regression analysis produced the following tables.  Coefficients  Stardard Error t Statistic p-value  Intercept 707.9144435.11831.6269470.114567x12.90330781.628020.0355680.971871x1211.912973.8062113.1298780.003967\begin{array} { | c | c | c | c | c | } \hline & \text { Coefficients } & \text { Stardard Error } & t \text { Statistic } & p \text {-value } \\\hline \text { Intercept } & 707.9144 & 435.1183 & 1.626947 & 0.114567 \\\hline \boldsymbol { x } _ { 1 } & 2.903307 & 81.62802 & 0.035568 & 0.971871 \\\hline \mathbf { x } _ { 1 } { } ^ { 2 } & 11.91297 & 3.806211 & 3.129878 & 0.003967 \\\hline\end{array}  Df  SS  MS Fp-value  Regression 2320551531602757747.345571.49E09 Residual 279140128338523.3 Total 2941195281\begin{array} { | c | c | c | c | c | c | } \hline & \text { Df } & \text { SS } & \text { MS } & F & p \text {-value } \\\hline \text { Regression } & 2 & 32055153 & 16027577 & 47.34557 & 1.49 \mathrm { E } - 09 \\\hline \text { Residual } & 27 & 9140128 & 338523.3 & & \\\hline \text { Total } & 29 & 41195281 & & & \\\hline\end{array} The regression equation for this analysis is ____________.


A)  A local parent group was concerned with the increasing cost of school for families with school aged children.The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year.They performed a multiple regression analysis using total cost as the dependent variable and academic year (x<sub>1</sub>) as the independent variables.The multiple regression analysis produced the following tables.  \begin{array} { | c | c | c | c | c | }  \hline & \text { Coefficients } & \text { Stardard Error } & t \text { Statistic } & p \text {-value } \\ \hline \text { Intercept } & 707.9144 & 435.1183 & 1.626947 & 0.114567 \\ \hline \boldsymbol { x } _ { 1 } & 2.903307 & 81.62802 & 0.035568 & 0.971871 \\ \hline \mathbf { x } _ { 1 } { } ^ { 2 } & 11.91297 & 3.806211 & 3.129878 & 0.003967 \\ \hline \end{array}   \begin{array} { | c | c | c | c | c | c | }  \hline & \text { Df } & \text { SS } & \text { MS } & F & p \text {-value } \\ \hline \text { Regression } & 2 & 32055153 & 16027577 & 47.34557 & 1.49 \mathrm { E } - 09 \\ \hline \text { Residual } & 27 & 9140128 & 338523.3 & & \\ \hline \text { Total } & 29 & 41195281 & & & \\ \hline \end{array}  The regression equation for this analysis is ____________. A)  = 707.9144 + 2.903307 x<sub>1</sub> + 11.91297 x<sub>1</sub><sup>2</sup> B)  = 707.9144 + 435.1183 x<sub>1</sub> + 1.626947 x<sub>1</sub><sup>2</sup> C)   = 435.1183 + 81.62802 x<sub>1</sub> + 3.806211 x<sub>1</sub><sup>2</sup> D)   = 1.626947 + 0.035568 x<sub>1</sub> + 3.129878 x<sub>1</sub><sup>2</sup> E)   = 1.626947 + 0.035568 x<sub>1</sub> - 3.129878 x<sub>1</sub><sup>2</sup> = 707.9144 + 2.903307 x1 + 11.91297 x12
B) 11efcd21_6411_aee5_b057_4518c9fddc20_TB7041_00= 707.9144 + 435.1183 x1 + 1.626947 x12
C) 11efcd21_6411_aee5_b057_4518c9fddc20_TB7041_00 = 435.1183 + 81.62802 x1 + 3.806211 x12
D) 11efcd21_6411_aee5_b057_4518c9fddc20_TB7041_00 = 1.626947 + 0.035568 x1 + 3.129878 x12
E) 11efcd21_6411_aee5_b057_4518c9fddc20_TB7041_00 = 1.626947 + 0.035568 x1 - 3.129878 x12

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