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

question 6

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The following MINITAB output presents a multiple regression equatior y^\hat { y } =b0+b1x1+b2x2+b3x3+b4x4
The regression equation is
Y=4.7712+0.2662X1+1.2710X21.1349X31.8545X4\mathrm { Y } = 4.7712 + 0.2662 \mathrm { X } 1 + 1.2710 \mathrm { X } 2 - 1.1349 \mathrm { X } 3 - 1.8545 \mathrm { X } 4
 Predictor  Coef  SE Coef  T P Constant 4.77120.76480.928000.3280X10.26620.80360.937400.3570X21.27100.84511.741700.0830X31.13490.63182.949900.0080X41.85450.67533.272000.0020\begin{array}{lllll}\text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \mathrm{P} \\\text { Constant } & 4.7712 & 0.7648 & 0.92800 & 0.3280 \\\mathrm{X} 1 & 0.2662 & 0.8036 & -0.93740 & 0.3570 \\\mathrm{X} 2 & 1.2710 & 0.8451 & 1.74170 & 0.0830 \\\mathrm{X} 3 & -1.1349 & 0.6318 & -2.94990 & 0.0080 \\\mathrm{X} 4 & -1.8545 & 0.6753 & 3.27200 & 0.0020\end{array}

 The following MINITAB output presents a multiple regression equatior  \hat { y } =b<sub>0</sub>+b<sub>1</sub>x<sub>1</sub>+b<sub>2</sub>x<sub>2</sub>+b<sub>3</sub>x<sub>3</sub>+b<sub>4</sub>x<sub>4</sub> The regression equation is  \mathrm { Y } = 4.7712 + 0.2662 \mathrm { X } 1 + 1.2710 \mathrm { X } 2 - 1.1349 \mathrm { X } 3 - 1.8545 \mathrm { X } 4   \begin{array}{lllll} \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \mathrm{P} \\ \text { Constant } & 4.7712 & 0.7648 & 0.92800 & 0.3280 \\ \mathrm{X} 1 & 0.2662 & 0.8036 & -0.93740 & 0.3570 \\ \mathrm{X} 2 & 1.2710 & 0.8451 & 1.74170 & 0.0830 \\ \mathrm{X} 3 & -1.1349 & 0.6318 & -2.94990 & 0.0080 \\ \mathrm{X} 4 & -1.8545 & 0.6753 & 3.27200 & 0.0020 \end{array}       \text { Analysis of Variance }   \begin{array}{lccccc} \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \\ \text { Regression } & 4 & 624.2 & 156.1 & 9.8797 & 0.003 \\ \text { Residual Error } & 40 & 633.7 & 15.8 & & \\ \text { Total } & 44 & 1,257.9 & & & \\ \hline \end{array}    It is desired to drop one of the explanatory variables. Which of the following is the most appropriate action? A)  Drop x<sub>4</sub>, then see whether R<sup>2</sup> increases B)  Drop  x<sub>1</sub>, then see whether R<sup>2</sup> increases C)  Drop x<sub>4</sub>, then see whether adjusted R<sup>2</sup> increases D)  Drop  x<sub>1</sub>, then see whether adjusted R<sup>2</sup> increases

 Analysis of Variance \text { Analysis of Variance }
 Source  DF  SS  MS  F  P  Regression 4624.2156.19.87970.003 Residual Error 40633.715.8 Total 441,257.9\begin{array}{lccccc}\text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \\\text { Regression } & 4 & 624.2 & 156.1 & 9.8797 & 0.003 \\\text { Residual Error } & 40 & 633.7 & 15.8 & & \\\text { Total } & 44 & 1,257.9 & & & \\\hline\end{array}


It is desired to drop one of the explanatory variables. Which of the following is the most appropriate action?


Definitions:

Binding Minimum Wage

A government-set minimum wage that is above the equilibrium wage, potentially leading to unemployment because the quantity of labor supplied exceeds the quantity demanded.

Labor Demanded

The total number of workers that employers are willing and able to hire at a given wage rate in a certain period of time.

Labor Supplied

The aggregate amount of hours that employees are prepared and capable of working for a specified rate of pay.

Binding Price Floor

A minimum price set by the government or body above the equilibrium price, leading to excess supply if the market cannot legally adjust to its equilibrium.

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