A multiple regression analysis produced the following tables. $$\begin{array} { | c | c | c | c | c | } \hline & \text { Coefficients } & \text { Stardard Error } & t \text { Statistic } & p \text {-value } \ \hline \text { Irtercept } & 1411.876 & 7.621533 & & \ \hline \boldsymbol { x } _ { 1 } & 35.18215 & 96.8433 & & \ \hline \boldsymbol { x } _ { 1 } ^ { 2 } & - 7.721648 & \mathbf { 3 . 0 0 7 9 4 3 } & & \ \hline \end{array}$$ $$\begin{array} { | c | c | c | c | c | } \hline & \mathrm { df } & \mathrm { SS } & \mathrm { MS } & F \ \hline \text { Regression } & 2 & 58567032 & 29283516 & 57.34861 \ \hline \text { Residual } & 25 & 12765573 & 510622.9 & \ \hline \text { Total } & 27 & 71332605 & & \ \hline \end{array}$$ If the predicted value of the dependent variable is 1000, then x₁ = ______. A)4.50666 B)8.158666 C)9.928668 D)10.15866 E)11.928666

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The Answer of A multiple regression analysis produced the following tables. $$\begin{array} { | c | c | c | c | c | } \hline & \text { Coefficients } & \text { Stardard Error } & t \text { Statistic } & p \text {-value } \ \hline \text { Irtercept } & 1411.876 & 7.621533 & & \ \hline \boldsymbol { x } _ { 1 } & 35.18215 & 96.8433 & & \ \hline \boldsymbol { x } _ { 1 } ^ { 2 } & - 7.721648 & \mathbf { 3 . 0 0 7 9 4 3 } & & \ \hline \end{array}$$ $$\begin{array} { | c | c | c | c | c | } \hline & \mathrm { df } & \mathrm { SS } & \mathrm { MS } & F \ \hline \text { Regression } & 2 & 58567032 & 29283516 & 57.34861 \ \hline \text { Residual } & 25 & 12765573 & 510622.9 & \ \hline \text { Total } & 27 & 71332605 & & \ \hline \end{array}$$ If the predicted value of the dependent variable is 1000, then x₁ = ______. A)4.50666 B)8.158666 C)9.928668 D)10.15866 E)11.928666

A Multiple Regression Analysis Produced the Following Tables If the Predicted Value of the Dependent Variable Is 1000