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TABLE 13-12
the Manager of the Purchasing Department of a Large

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TABLE 13-12
The manager of the purchasing department of a large banking organization would like to develop a model to predict the amount of time (measured in hours) it takes to process invoices. Data are collected from a sample of 30 days, and the number of invoices processed and completion time in hours is recorded. Below is the regression output:
 Regression Statistics  Multiple R 0.9947 R Square 0.8924 Adjusted R Square 0.8886 Standard Error 0.3342 ations 30\begin{array}{l}\text { Regression Statistics }\\\begin{array} { l c } \hline \text { Multiple R } & 0.9947 \\\text { R Square } & 0.8924 \\\text { Adjusted R Square } & 0.8886 \\\text { Standard Error } & 0.3342 \\\text { ations } & 30 \\\hline\end{array}\end{array}

 d f  SS MS F  Significance F Regression125.943825.9438232.22004.3946E15Residual 283.12820.1117Total 2929.072\begin{array}{lrrccc}\hline & \text { d f } & \text { SS } & \text {MS} & \text { F } & \text { Significance F } \\\hline \text {Regression} & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm{E}-15 \\ \text {Residual }& 28 & 3.1282 & 0.1117 & & \\ \text {Total }& 29 & 29.072 & & & \\\hline\end{array}

 Coefficients  Standard Error  t Stat  p -valueLower 95%Upper 95% Invoices 0.40240.12363.25590.00300.14920.6555Processed 0.01260.000815.23884.3946E150.01090.0143\begin{array}{lrrrrrr}\hline & \text { Coefficients }& \text { Standard Error }& \text { t Stat }& \text { p -value}& \text {Lower 95\%} & \text {Upper 95\%} \\\hline \text { Invoices }& 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \\ \text {Processed }& 0.0126 & 0.0008 & 15.2388 & 4.3946 \mathrm{E}-15 & 0.0109 & 0.0143 \\\hline\end{array}

 TABLE 13-12 The manager of the purchasing department of a large banking organization would like to develop a model to predict the amount of time (measured in hours) it takes to process invoices. Data are collected from a sample of 30 days, and the number of invoices processed and completion time in hours is recorded. Below is the regression output:    \begin{array}{l} \text { Regression Statistics }\\ \begin{array} { l c }  \hline \text { Multiple R } & 0.9947 \\ \text { R Square } & 0.8924 \\ \text { Adjusted R Square } & 0.8886 \\ \text { Standard Error } & 0.3342 \\ \text { ations } & 30 \\ \hline \end{array} \end{array}     \begin{array}{lrrccc} \hline & \text { d f } &  \text { SS } &  \text {MS} &  \text { F } & \text { Significance  F } \\ \hline  \text {Regression} & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm{E}-15  \\  \text {Residual }& 28 & 3.1282 & 0.1117 & & \\  \text {Total }& 29 & 29.072 & & & \\ \hline \end{array}    \begin{array}{lrrrrrr} \hline & \text { Coefficients }& \text { Standard Error }& \text { t Stat }&  \text { p -value}& \text {Lower 95\%} &  \text {Upper 95\%} \\ \hline \text { Invoices }& 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \\  \text {Processed }& 0.0126 & 0.0008 & 15.2388 &  4.3946 \mathrm{E}-15  & 0.0109 & 0.0143 \\ \hline \end{array}          -Referring to Table 13-12, there is a 95% probability that the average amount of time needed to process one additional invoice is somewhere between 0.0109 and 0.0143 hours.

 TABLE 13-12 The manager of the purchasing department of a large banking organization would like to develop a model to predict the amount of time (measured in hours) it takes to process invoices. Data are collected from a sample of 30 days, and the number of invoices processed and completion time in hours is recorded. Below is the regression output:    \begin{array}{l} \text { Regression Statistics }\\ \begin{array} { l c }  \hline \text { Multiple R } & 0.9947 \\ \text { R Square } & 0.8924 \\ \text { Adjusted R Square } & 0.8886 \\ \text { Standard Error } & 0.3342 \\ \text { ations } & 30 \\ \hline \end{array} \end{array}     \begin{array}{lrrccc} \hline & \text { d f } &  \text { SS } &  \text {MS} &  \text { F } & \text { Significance  F } \\ \hline  \text {Regression} & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm{E}-15  \\  \text {Residual }& 28 & 3.1282 & 0.1117 & & \\  \text {Total }& 29 & 29.072 & & & \\ \hline \end{array}    \begin{array}{lrrrrrr} \hline & \text { Coefficients }& \text { Standard Error }& \text { t Stat }&  \text { p -value}& \text {Lower 95\%} &  \text {Upper 95\%} \\ \hline \text { Invoices }& 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \\  \text {Processed }& 0.0126 & 0.0008 & 15.2388 &  4.3946 \mathrm{E}-15  & 0.0109 & 0.0143 \\ \hline \end{array}          -Referring to Table 13-12, there is a 95% probability that the average amount of time needed to process one additional invoice is somewhere between 0.0109 and 0.0143 hours.

-Referring to Table 13-12, there is a 95% probability that the average amount of time needed to process one additional invoice is somewhere between 0.0109 and 0.0143 hours.

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Definitions:

Standard Deviation

A statistic that denotes the dispersion or variability of a dataset relative to its mean, indicating how spread out the data points are.

Margin of Error

A statistic expressing the amount of random sampling error in a survey's results, indicating how much you can expect the survey results to reflect the views of the overall population.

Probability

The measure of the likelihood that a particular event will occur, expressed as a number between 0 and 1.

Standard Error

Indicates the standard deviation of the sampling distribution of a statistic, most commonly the mean.

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