Solved

Instruction 12 $\text { Regression statistics }$ $\text { ANOVA }$

question 47

Multiple Choice

Instruction 12.35
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:
$\text { Regression statistics }$
$\begin{array}{|l|l|}\hline \text { MultipleR } & 0.8691 \\\hline \text { R Square } & 0.7554 \\\hline \begin{array}{l}\text { Adjusted R } \\\text { Square }\end{array} & 0.7467 \\\hline \text { Standard Error } & 44.4765 \\\hline \text { Observations } & 30.0000\\\hline \end{array}$

$\text { ANOVA }$
$\begin{array}{|l|l|l|l|l|l|}\hline & d f & S S & M S & F & \begin{array}{l}\text { Significance } \\F\end{array} \\\hline \text { Regression } & 1 & 171062.9193 & 171062.9193 & 86.4759 & 0.0000 \\\hline \text { Residual } & 28 & 55388.4309 & 1978.1582 & & \\\hline \text { Total } & 29 & 226451.3503 & & & \\\hline\end{array}$

$\begin{array}{|l|l|l|l|l|l|l|}\hline & \text { Coefficients } & \begin{array}{l}\text { Standard } \\\text { Error }\end{array} & \text { t Stat } & p \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\\hline \text { Intercept } & -95.0614 & 26.9183 & -3.5315 & 0.0015 & -150.2009 & -39.9218 \\\hline \text { Download } & 3.7297 & 0.4011 & 9.2992 & 0.0000 & 2.9082 & 4.5513 \\\hline\end{array}$

$Instruction 12.35 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: \text { Regression statistics } \begin{array}{|l|l|} \hline \text { MultipleR } & 0.8691 \\ \hline \text { R Square } & 0.7554 \\ \hline \begin{array}{l} \text { Adjusted R } \\ \text { Square } \end{array} & 0.7467 \\ \hline \text { Standard Error } & 44.4765 \\ \hline \text { Observations } & 30.0000\\ \hline \end{array} \text { ANOVA } \begin{array}{|l|l|l|l|l|l|} \hline & d f & S S & M S & F & \begin{array}{l} \text { Significance } \\ F \end{array} \\ \hline \text { Regression } & 1 & 171062.9193 & 171062.9193 & 86.4759 & 0.0000 \\ \hline \text { Residual } & 28 & 55388.4309 & 1978.1582 & & \\ \hline \text { Total } & 29 & 226451.3503 & & & \\ \hline \end{array} \begin{array}{|l|l|l|l|l|l|l|} \hline & \text { Coefficients } & \begin{array}{l} \text { Standard } \\ \text { Error } \end{array} & \text { t Stat } & p \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & -95.0614 & 26.9183 & -3.5315 & 0.0015 & -150.2009 & -39.9218 \\ \hline \text { Download } & 3.7297 & 0.4011 & 9.2992 & 0.0000 & 2.9082 & 4.5513 \\ \hline \end{array} -Referring to Instruction 12.35,which of the following is the correct null hypothesis for testing whether there is a linear relationship between revenue and number of downloads? A) H0: b1 = 0 B) H0: b1 ? 0 C) H0: ?1 ? 0 D) H0: ?1 = 0$ $Instruction 12.35 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: \text { Regression statistics } \begin{array}{|l|l|} \hline \text { MultipleR } & 0.8691 \\ \hline \text { R Square } & 0.7554 \\ \hline \begin{array}{l} \text { Adjusted R } \\ \text { Square } \end{array} & 0.7467 \\ \hline \text { Standard Error } & 44.4765 \\ \hline \text { Observations } & 30.0000\\ \hline \end{array} \text { ANOVA } \begin{array}{|l|l|l|l|l|l|} \hline & d f & S S & M S & F & \begin{array}{l} \text { Significance } \\ F \end{array} \\ \hline \text { Regression } & 1 & 171062.9193 & 171062.9193 & 86.4759 & 0.0000 \\ \hline \text { Residual } & 28 & 55388.4309 & 1978.1582 & & \\ \hline \text { Total } & 29 & 226451.3503 & & & \\ \hline \end{array} \begin{array}{|l|l|l|l|l|l|l|} \hline & \text { Coefficients } & \begin{array}{l} \text { Standard } \\ \text { Error } \end{array} & \text { t Stat } & p \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & -95.0614 & 26.9183 & -3.5315 & 0.0015 & -150.2009 & -39.9218 \\ \hline \text { Download } & 3.7297 & 0.4011 & 9.2992 & 0.0000 & 2.9082 & 4.5513 \\ \hline \end{array} -Referring to Instruction 12.35,which of the following is the correct null hypothesis for testing whether there is a linear relationship between revenue and number of downloads? A) H0: b1 = 0 B) H0: b1 ? 0 C) H0: ?1 ? 0 D) H0: ?1 = 0$
-Referring to Instruction 12.35,which of the following is the correct null hypothesis for testing whether there is a linear relationship between revenue and number of downloads?

Definitions:

Complete Performance

The full and exact fulfillment of all terms and conditions as outlined in a contract or agreement.

Unconditional Offer

An offer made by one party to another that requires no additional conditions or stipulations to be met for acceptance.

Tender

An unconditional offer to perform an obligation by a person who is ready, willing, and able to do so.

Tendered

Offered formally or presented for acceptance, like a payment or a resignation.

Instruction 12 Regression statistics \text { Regression statistics } Regression statistics ANOVA \text { ANOVA } ANOVA

Definitions:

Instruction 12 $\text { Regression statistics }$ $\text { ANOVA }$