The Following MINITAB Output Presents a Multiple Regression Equation $\hat { y } = b _ { 0 } + b _ { 1 } x _ { 1 } +$

Multiple Choice

The following MINITAB output presents a multiple regression equation $\hat { y } = b _ { 0 } + b _ { 1 } x _ { 1 } +$ $b _ { 2 } x _ { 2 } + b _ { 3 } x _ { 3 } + b _ { 4 } x _ { 4 }$
The regression equation is
$$\mathrm { Y } = 1.9568 + 1.7369 \mathrm { X } 1 + 1.1099 \mathrm { X } 2 - 1.2672 \mathrm { X } 3 + 1.6080 \mathrm { X } 4$$
$\begin{array}{lllll}\text { Predictor } & \text { Coef } & \text { SE Coef } & \mathrm{T} & \mathrm{P} \\\text { Constant } & 1.9568 & 0.8248 & 1.1277 & 0.345 \\\mathrm{X} 1 & 1.7369 & 0.7980 & 3.4296 & 0.004 \\\mathrm{X} 2 & 1.1099 & 0.7500 & -3.2529 & 0.006 \\\mathrm{X} 3 & -1.2672 & 0.7534 & 1.8730 & 0.076 \\\mathrm{X} 4 & 1.6080 & 0.8733 & -0.9328 & 0.349\end{array}$

$\mathrm{S}=2.5685 \quad \mathrm{R}-\mathrm{Sq}=33.7 \% \quad \mathrm{R}-\mathrm{Sq}(\mathrm{adj}) =26.1 \%$

$\begin{array}{l}\text { Analysis of Variance }\\\begin{array}{lcccrl}\text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F P } & \\\text { Regression } & 4 & 503.9 & 126.0 & 5.0806 & 0.003 \\\text { Residual Error } & 40 & 990.4 & 24.8 & & \\\text { Total } & 44 & 1494.3 & & & \\\hline\end{array}\end{array}$
Predict the value of y when $x _ { 1 } = 1 , x _ { 2 } = 2 , x _ { 3 } = 3 , x _ { 4 } = 6$

A) 9.8031
B) 11.7599
C) 10.6228
D) 9.798