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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?


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An account that carries its ending balance over to the next accounting period, typically representing real accounts like assets, liabilities, and equity.

Closing Process

The steps taken at the end of an accounting period to prepare accounts for the next period, including zeroing out temporary accounts.

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Closing the books is an accounting process involving updating ledger accounts at the end of an accounting period to prepare financial statements.

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