A multiple regression analysis produced the following tables.

\begin{array} { | c | c | c | c | c | } \hline \text { Predictor } & \text { Coefficients } & \text { Stardard Error } & \boldsymbol { t } \text { Statistic } & p \text {-value } \\\hline \text { Irtercept } & - 139.609 & 2548.989 & - 0.05477 & 0.957154 \\\hline \boldsymbol { x } _ { 1 } & 24.24619 & 22.25267 & 1.089586 & \mathbf { 0 . 2 9 5 6 8 2 } \\\hline \mathbf { x } _ { 2 } & 32.10171 & 17.44559 & 1.840105 & 0.08869 \\\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 & 302689 & 151344.5 & 1.705942 & 0.219838 \\\hline \text { Residual } & 13 & 1153309 & 88716.07 & & \\\hline \text { Total } & 15 & 1455998 & & & \\\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 the only predictor variable significant at the 5% level
D)x₂ is the only predictor variable significant at the 5% level
E)all variables are significant at 5% level

In the Model Y = β\betaβ 0 β\betaβ 1x1

True/False

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In the Model Y = $\beta$ ₀ $\beta$ ₁x₁

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