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The Following MINITAB Output Presents a Multiple Regression Equatior $\hat { y }$

question 19

Multiple Choice

The following MINITAB output presents a multiple regression equatior $\hat { y }$ =b₀+b₁x₁+b₂x₂+b₃x₃+b₄x₄
The regression equation is
$\mathrm { Y } = 5.5079 + 1.6552 \mathrm { X } 1 - 1.1088 \mathrm { X } 2 + 1.3981 \mathrm { X } 3 - 1.2465 \mathrm { X } 4$

$\begin{array}{lllll}\text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \\\text { Constant } & 5.5079 & 0.7640 & 1.1002 & 0.314 \\\text { X1 } & 1.6552 & 0.7032 & 3.1929 & 0.002 \\\text { X2 } & -1.1088 & 0.6023 & -3.2310 & 0.005 \\\text { X3 } & 1.3981 & 0.8970 & 1.8137 & 0.087 \\\text { X4 } & -1.2465 & 0.8251 & -1.1433 & 0.354\end{array}$

$The following MINITAB output presents a multiple regression equatior \hat { y } =b0+b1x1+b2x2+b3x3+b4x4 The regression equation is \mathrm { Y } = 5.5079 + 1.6552 \mathrm { X } 1 - 1.1088 \mathrm { X } 2 + 1.3981 \mathrm { X } 3 - 1.2465 \mathrm { X } 4 \begin{array}{lllll} \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \\ \text { Constant } & 5.5079 & 0.7640 & 1.1002 & 0.314 \\ \text { X1 } & 1.6552 & 0.7032 & 3.1929 & 0.002 \\ \text { X2 } & -1.1088 & 0.6023 & -3.2310 & 0.005 \\ \text { X3 } & 1.3981 & 0.8970 & 1.8137 & 0.087 \\ \text { X4 } & -1.2465 & 0.8251 & -1.1433 & 0.354 \end{array} \text { Analysis of Variance } \begin{array}{lccccc} \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \\ \text { Regression } & 4 & 637.5 & 159.4 & 7.1480 & 0.003 \\ \text { Residual Error } & 40 & 893.2 & 22.3 & & \\ \text { Total } & 44 & 1,530.7 & & & \end{array} Let \beta _ { 1 } be the coefficient X _ { 1 } Test the hypothesis H _ { 0 } : \beta _ { 1 } = 0 rersus H _ { 1 } : \beta _ { 1 } \neq 0 \text { at the } \alpha = 0.05 level. What do you conclude? A) Do not H0 B) Reject H0$

$\text { Analysis of Variance }$
$\begin{array}{lccccc}\text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \\\text { Regression } & 4 & 637.5 & 159.4 & 7.1480 & 0.003 \\\text { Residual Error } & 40 & 893.2 & 22.3 & & \\\text { Total } & 44 & 1,530.7 & & &\end{array}$

Let $\beta _ { 1 }$ be the coefficient $X _ { 1 }$ Test the hypothesis $H _ { 0 } : \beta _ { 1 } = 0$ rersus $H _ { 1 } : \beta _ { 1 } \neq 0 \text { at the } \alpha = 0.05$
level. What do you conclude?

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The Following MINITAB Output Presents a Multiple Regression Equatior y^\hat { y }y^​

Definitions:

The Following MINITAB Output Presents a Multiple Regression Equatior $\hat { y }$