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

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SCENARIO 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:
 SCENARIO 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}{lr} {\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}    \text { ANOVA }    \begin{array}{|l|r|r|r|r|r|} \hline &\text { df } & \text { SS } & \text { MS } & F & \text { 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}          Simple Linear Regression 12-41   -Referring to Scenario 12-11, what is the standard deviation around the regression line?  Regression Statistics  Multiple R 0.8691 R Square 0.7554 Adjusted R Square 0.7467 Standard Error 44.4765 Observations 30.0000\begin{array}{lr}{\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 \text { ANOVA }
 df  SS  MS F Significance F Regression 1171062.9193171062.919386.47590.0000 Residual 2855388.43091978.1582 Total 29226451.3503\begin{array}{|l|r|r|r|r|r|}\hline &\text { df } & \text { SS } & \text { MS } & F & \text { 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}


 SCENARIO 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}{lr} {\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}    \text { ANOVA }    \begin{array}{|l|r|r|r|r|r|} \hline &\text { df } & \text { SS } & \text { MS } & F & \text { 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}          Simple Linear Regression 12-41   -Referring to Scenario 12-11, what is the standard deviation around the regression line?


 SCENARIO 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}{lr} {\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}    \text { ANOVA }    \begin{array}{|l|r|r|r|r|r|} \hline &\text { df } & \text { SS } & \text { MS } & F & \text { 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}          Simple Linear Regression 12-41   -Referring to Scenario 12-11, what is the standard deviation around the regression line? Simple Linear Regression 12-41  SCENARIO 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}{lr} {\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}    \text { ANOVA }    \begin{array}{|l|r|r|r|r|r|} \hline &\text { df } & \text { SS } & \text { MS } & F & \text { 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}          Simple Linear Regression 12-41   -Referring to Scenario 12-11, what is the standard deviation around the regression line?
-Referring to Scenario 12-11, what is the standard deviation around the regression line?


Definitions:

Critical Thinking

The objective analysis and evaluation of an issue in order to form a judgement, characterized by reason and evidence-based assessment.

Appraising

The process of evaluating the significance of situations, stimuli, or events in relation to one’s own well-being.

Evaluating Evidence

The process of critically assessing the reliability, validity, and relevance of data or information in support of conclusions or theories.

Empirical Approach

A method of acquiring knowledge based on observation, experimentation, and experience, rather than through theory or belief.

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