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The Following MINITAB Output Presents a Multiple Regression Equation y^=b0+b1x1+b2x2+b3x3\hat { y } = b _ { 0 } + b _ { 1 } x _ { 1 } + b _ { 2 } x _ { 2 } + b _ { 3 } x _ { 3 }

question 31

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The following MINITAB output presents a multiple regression equation y^=b0+b1x1+b2x2+b3x3\hat { y } = b _ { 0 } + b _ { 1 } x _ { 1 } + b _ { 2 } x _ { 2 } + b _ { 3 } x _ { 3 } +b4x4+ b _ { 4 } x _ { 4 } .
The regression equation is
Y=1.9568+1.7369X1+1.1099X21.2672X3+1.6080X4\mathrm { Y } = 1.9568 + 1.7369 \mathrm { X } 1 + 1.1099 \mathrm { X } 2 - 1.2672 \mathrm { X } 3 + 1.6080 \mathrm { X } 4
 Predictor  Coef  SE Coef  T  P  Constant 1.95680.82481.12770.345 X1 1.73690.79803.42960.004 X2 1.10990.75003.25290.006 X3 1.26720.75341.87300.076 X4 1.60800.87330.93280.349\begin{array}{lllll}\text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \\\text { Constant } & 1.9568 & 0.8248 & 1.1277 & 0.345 \\\text { X1 } & 1.7369 & 0.7980 & 3.4296 & 0.004 \\\text { X2 } & 1.1099 & 0.7500 & -3.2529 & 0.006 \\\text { X3 } & -1.2672 & 0.7534 & 1.8730 & 0.076 \\\text { X4 } & 1.6080 & 0.8733 & -0.9328 & 0.349\end{array}
 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 } & \text { T } & \text { P } \\ \text { Constant } & 1.9568 & 0.8248 & 1.1277 & 0.345 \\ \text { X1 } & 1.7369 & 0.7980 & 3.4296 & 0.004 \\ \text { X2 } & 1.1099 & 0.7500 & -3.2529 & 0.006 \\ \text { X3 } & -1.2672 & 0.7534 & 1.8730 & 0.076 \\ \text { X4 } & 1.6080 & 0.8733 & -0.9328 & 0.349 \end{array}      \text { Analysis of Variance }   \begin{array}{lccccc} \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \\ \text { Regression } & 4 & 503.9 & 126.0 & 5.0806 & 0.003 \\ \text { Residual Error } & 40 & 990.4 & 24.8 & & \\ \text { Total } & 44 & 1,494.3 & & & \\ \hline \end{array}   Predict the value of  \mathrm { y }  when  x _ { 1 } = 1 , x _ { 2 } = 2 , x _ { 3 } = 3 , x _ { 4 } = 6  A)  9.798 B)  9.8031 C)  10.6228 D)  11.7599

 Analysis of Variance \text { Analysis of Variance }
 Source  DF  SS  MS  F  P  Regression 4503.9126.05.08060.003 Residual Error 40990.424.8 Total 441,494.3\begin{array}{lccccc}\text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \\\text { Regression } & 4 & 503.9 & 126.0 & 5.0806 & 0.003 \\\text { Residual Error } & 40 & 990.4 & 24.8 & & \\\text { Total } & 44 & 1,494.3 & & & \\\hline\end{array}

Predict the value of y\mathrm { y } when x1=1,x2=2,x3=3,x4=6x _ { 1 } = 1 , x _ { 2 } = 2 , x _ { 3 } = 3 , x _ { 4 } = 6


Definitions:

Favourable Variances

Differences between actual costs and budgeted costs that result in a better-than-expected financial performance, often indicating cost savings or higher revenues.

Large Variances

Significant differences between planned and actual figures in a budget, project, or any performance measurement, indicating greater deviations from expectations.

Consistent Trends

Patterns or changes in data that continue over a period of time in a similar manner.

Standard Direct Labour Hours

The estimated amount of labor hours required to produce a certain amount of output under normal conditions.

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