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}

These results indicate that ____________.
A)none of the predictor variables are significant at the 5% level
B)each predictor variable is significant at the 5% level
C)x₁ is significant at the 5% level
D)x₂ is significant at the 5% level
E)the intercept is not significant at 5% level

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