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Instruction 12-11
a Computer Software Developer Would Like to Use

question 71

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Instruction 12-11
A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware.Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed:
 Regression Statistics  Multiple R 0.8691 R Square 0.7554 Adjusted R Square 0.7467 Standard Error 44.4765 Observations 30.0000\begin{array}{l|r}\hline {\text { Regression Statistics }} \\\hline \text { Multiple R } & 0.8691 \\\hline \text { R Square } & 0.7554 \\\hline \text { Adjusted R Square } & 0.7467 \\\hline \text { Standard Error } & 44.4765 \\\hline \text { Observations } & 30.0000 \\\hline\end{array}
ANOVA
dfSSMSFsignificanceF Regression 1171062.9193171062.919386.47590.0000 Residual 2855388.43091978.1582 Total 29226451.3503\begin{array}{|l|r|r|r|r|r|}\hline &df&SS&MS&F&significance F\\\hline \text { Regression } & 1 & 171062.9193 & 171062.9193 & 86.4759 & 0.0000 \\\hline \text { Residual } & 28 & 55388.4309 & 1978.1582 & & \\\hline \text { Total } & 29 & 226451.3503 & & & \\\hline\end{array}

 Coefficients  Standard Error t Stat  P-value  Lower 95%  Upper 95%  Intercept 95.061426.91833.53150.0015150.200939.9218 Download 3.72970.40119.29920.00002.90824.5513\begin{array}{lrrrrrrr}\hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \\\hline \text { Intercept } & -95.0614 & 26.9183 & -3.5315 & 0.0015 & -150.2009 & -39.9218 \\\text { Download } & 3.7297 & 0.4011 & 9.2992 & 0.0000 & 2.9082 & 4.5513 \\\hline\end{array}  Instruction 12-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware.Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed:   \begin{array}{l|r} \hline {\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.8691 \\ \hline \text { R Square } & 0.7554 \\ \hline \text { Adjusted R Square } & 0.7467 \\ \hline \text { Standard Error } & 44.4765 \\ \hline \text { Observations } & 30.0000 \\ \hline \end{array}  ANOVA  \begin{array}{|l|r|r|r|r|r|} \hline &df&SS&MS&F&significance F\\ \hline \text { Regression } & 1 & 171062.9193 & 171062.9193 & 86.4759 & 0.0000 \\ \hline \text { Residual } & 28 & 55388.4309 & 1978.1582 & & \\ \hline \text { Total } & 29 & 226451.3503 & & & \\ \hline \end{array}    \begin{array}{lrrrrrrr} \hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & -95.0614 & 26.9183 & -3.5315 & 0.0015 & -150.2009 & -39.9218 \\ \text { Download } & 3.7297 & 0.4011 & 9.2992 & 0.0000 & 2.9082 & 4.5513 \\ \hline \end{array}      -Referring to Instruction 12-11,which of the following is the correct interpretation for the slope coefficient? A) For each increase of 1 million dollars in box office gross,expected home video units sold are estimated to increase by 4.3331 thousand units. B) For each increase of 1 dollar in box office gross,expected home video units sold are estimated to increase by 4.3331 units. C) For each increase of 1 million dollars in box office gross,expected home video units sold are estimated to increase by 4.3331 units. D) For each increase of 1 dollar in box office gross,expected home video units sold are estimated to increase by 4.3331 thousand units.  Instruction 12-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware.Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed:   \begin{array}{l|r} \hline {\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.8691 \\ \hline \text { R Square } & 0.7554 \\ \hline \text { Adjusted R Square } & 0.7467 \\ \hline \text { Standard Error } & 44.4765 \\ \hline \text { Observations } & 30.0000 \\ \hline \end{array}  ANOVA  \begin{array}{|l|r|r|r|r|r|} \hline &df&SS&MS&F&significance F\\ \hline \text { Regression } & 1 & 171062.9193 & 171062.9193 & 86.4759 & 0.0000 \\ \hline \text { Residual } & 28 & 55388.4309 & 1978.1582 & & \\ \hline \text { Total } & 29 & 226451.3503 & & & \\ \hline \end{array}    \begin{array}{lrrrrrrr} \hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & -95.0614 & 26.9183 & -3.5315 & 0.0015 & -150.2009 & -39.9218 \\ \text { Download } & 3.7297 & 0.4011 & 9.2992 & 0.0000 & 2.9082 & 4.5513 \\ \hline \end{array}      -Referring to Instruction 12-11,which of the following is the correct interpretation for the slope coefficient? A) For each increase of 1 million dollars in box office gross,expected home video units sold are estimated to increase by 4.3331 thousand units. B) For each increase of 1 dollar in box office gross,expected home video units sold are estimated to increase by 4.3331 units. C) For each increase of 1 million dollars in box office gross,expected home video units sold are estimated to increase by 4.3331 units. D) For each increase of 1 dollar in box office gross,expected home video units sold are estimated to increase by 4.3331 thousand units.
-Referring to Instruction 12-11,which of the following is the correct interpretation for the slope coefficient?


Definitions:

Assembly Line Balancing

The process of optimizing the allocation of tasks among workers or machines in an assembly line to minimize production time and avoid idle time.

Workstations

Specific work areas designed for completing particular tasks within a manufacturing process or office setting, often equipped with necessary tools and equipment.

Heuristic Rule

A practical method or guideline designed to solve problems faster than conventional methods, though not always with a perfect outcome.

Product Layout

An arrangement where production equipment is organized according to the sequential order of the product's assembly steps, optimizing efficiencies in mass production.

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