A multiple regression analysis produced the following tables. $$\begin{array} { | c | c | c | c | c | } \hline \text { Predictor } & \text { Coefficients } & \text { Stardard Eror } & \boldsymbol { t } \text { Statistic } & p \text {-value } \ \hline \text { Irtercept } & 616.6849 & 154.5534 & 3.990108 & 0.000947 \ \hline \boldsymbol { x } _ { 1 } & - 3.33833 & \mathbf { 2 . 3 3 3 5 4 8 } & - 1.43058 & \mathbf { 0 . 1 7 0 6 7 5 } \ \hline \mathbf { x } _ { 2 } & 1.780075 & \mathbf { 0 . 3 3 5 6 0 5 } & 5.30407 & 5.83 \mathrm { E } - 05 \ \hline \end{array}$$ $$\begin{array} { | c | c | c | c | c | c | } \hline \text { Source } & \mathrm { df } & \text { SS } & \text { MS } & F & p \text {-value } \ \hline \text { Repression } & 2 & 121783 & 60891.48 & 14.76117 & 0.000286 \ \hline \text { Residual } & 15 & 61876.68 & 4125.112 & & \ \hline \text { Total } & 17 & 183659.6 & & & \ \hline \end{array}$$ The regression equation for this analysis is ____________. A) = 616.6849 + 3.33833 x₁ + 1.780075 x₂ B) = 154.5535 - 1.43058 x₁ + 5.30407 x₂ C) = 616.6849 - 3.33833 x₁ - 1.780075 x₂ D) = 154.5535 + 2.333548 x₁ + 0.335605 x₂ E) = 616.6849 - 3.33833 x₁ + 1.780075 x₂

Question

Quizplus · Accepted Answer

The regression equation is constructed using the coefficients from the table for each predictor. The intercept is 616.6849, the coefficient for $x_1$ is -3.33833, and the coefficient for $x_2$ is 1.780075, leading to the equation $ŷ = 616.6849 - 3.33833x_1 + 1.780075x_2$.

A Multiple Regression Analysis Produced the Following Tables The Regression Equation for This Analysis Is ____________