The Following MINITAB Output Presents a Multiple Regression Equation $y = b _ { 0 } + b _ { 1 } x _ { 1 } + b _ { 2 } x _ { 2 } + b _ { 3 } x _ { 3 }$

The following MINITAB output presents a multiple regression equation $y = b _ { 0 } + b _ { 1 } x _ { 1 } + b _ { 2 } x _ { 2 } + b _ { 3 } x _ { 3 }$ $+ b _ { 4 } x _ { 4 }$ .
The regression equation is
$$\mathrm { Y } = 3.9695 + 1.4577 \mathrm { X } 1 - 1.7859 \mathrm { X } 2 + 0.7686 \mathrm { X } 3 + 0.0777 \mathrm { X } 4$$
$\begin{array}{lllll}\text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \\\text { Constant } & 3.9695 & 0.8785 & 0.9299 & 0.327 \\\text { X1 } & 1.4577 & 0.6034 & 3.5107 & 0.003 \\\text { X2 } & -1.7859 & 0.7302 & -3.1148 & 0.005 \\\text { X3 } & 0.7686 & 0.6732 & 1.9294 & 0.088 \\\text { X4 } & 0.0777 & 0.7569 & -1.0782 & 0.352\end{array}$




$\text { Analysis of Variance }$
$\begin{array}{lccccc}\text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \\\text { Regression } & 4 & 1,148.7 & 287.2 & 9.0031 & 0.003 \\\text { Residual Error } & 34 & 1,083.9 & 31.9 & & \\\text { Total } & 38 & 2,232.6 & & & \\\hline\end{array}$
Is the model useful for prediction? Use the  = 0.05 level.