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

question 31

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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, the estimated average amount of time it takes to process one additional invoice is A)  0.0126 more hours. B)  0.0126 fewer hours. C)  0.4024 more hours. D)  0.4024 fewer 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, the estimated average amount of time it takes to process one additional invoice is A)  0.0126 more hours. B)  0.0126 fewer hours. C)  0.4024 more hours. D)  0.4024 fewer hours.

-Referring to Table 13-12, the estimated average amount of time it takes to process one additional invoice is


Definitions:

Discriminant Validity

The extent to which a construct is truly distinct from other constructs by empirical standards, often assessed through its lack of correlation with unrelated constructs.

Convergent Validity

When a scale correlates with other scales measuring the same construct.

Face Validity

When scale items appear, at face value, to measure what they are supposed to measure.

Convergent Validity

The degree to which two measures that theoretically should be related are actually related.

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