The Model $E ( y ) = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 }$

The model $E ( y ) = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 }$ was fit to a set of data.
A partial printout for the analysis follows:
$\begin{array}{rrrrrrrrr}\hline & & & \text { Actual } & \text { Predict } & & \text { Lower 95\% CL } & \text { Upper 95\% CL } \\\text { OBS } & \mathrm{X} 1 & \mathrm{X} 2 & \text { Value } & \text { Value } & \text { Residual } & \text { Predict } & \text { Predict } \\& & & & & & & \\1 & 7781 & 644 & 74.707 & 83.175 & -8.468 & 47.224 & 119.126 \\\hline\end{array}$

Interpret the value of the residual when $x _ { 1 } = 7,781$ and $x _ { 2 } = 644$ .
A) The predicted $\hat{y}$ exceeds the observed value of $y$ by $8.468$ .
B) The predicted $\hat{y}$ is $8.468$ less than the observed value of $y$ .
C) Since the residual is negative, there is evidence of a negative linear relationship between $y$ and at least one of the two independent variables.
D) Since the residual is not 0 , the model is not useful for predicting $y$ .