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

question 141

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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 Observations 30\begin{array} { l c } { \text { Regression } \text { Statistics } } \\\hline \text { Multiple R } & 0.9947 \\\text { R Square } & 0.8924 \\\text { Adjusted R Square } & 0.8886 \\\text { Standard Error } & 0.3342 \\\text { Observations } & 30 \\\hline\end{array}  ANOVA df SS  MS F Significance F Regression 125.943825.9438232.22004.3946E15 Residual 283.12820.1117 Total 2929.072\begin{array}{l}\text { ANOVA }\\\begin{array} { l c c c c c } & d f & \text { SS } & \text { MS } & F & \text { Significance } F \\\text { Regression } & 1 & 25.9438&25 .9438 & 232.2200&4 .3946 \mathrm { E } - 15 \\\text { Residual } & 28 & 3.12820 .1117 & \\\text { Total } & 29 & 29.072 \\\hline\end{array}\end{array}  Coefficients  Standard Enror t Stat p-value  Lower 95%  Upper 95%  Invoices 0.40240.12363.25590.00300.14920.6555 Processed 0.01260.000815.23884.3946E150.01090.0143\begin{array} { l r r r r r r } \hline & \text { Coefficients } & \text { Standard Enror } & t \text { Stat } & p \text {-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\end{array}  Coefficients  Standard Enor  t Stat p-value  Lower 95%  Upper 95%  Invoices 0.40240.12363.25590.00300.14920.6555\begin{array}{rrrrrrr} & \text { Coefficients } & \text { Standard Enor } & \text { t Stat } & p \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\\hline \text { Invoices } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555\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 c }   { \text { Regression } \text { Statistics } } \\ \hline \text { Multiple R } & 0.9947 \\ \text { R Square } & 0.8924 \\ \text { Adjusted R Square } & 0.8886 \\ \text { Standard Error } & 0.3342 \\ \text { Observations } & 30 \\ \hline \end{array}   \begin{array}{l} \text { ANOVA }\\ \begin{array} { l c c c c c }  & d f & \text { SS } & \text { MS } & F & \text { Significance } F \\ \text { Regression } & 1 & 25.9438&25 .9438 & 232.2200&4 .3946 \mathrm { E } - 15 \\ \text { Residual } & 28 & 3.12820 .1117 & \\ \text { Total } & 29 & 29.072 \\ \hline \end{array} \end{array}   \begin{array} { l r r r r r r }  \hline & \text { Coefficients } & \text { Standard Enror } & t \text { Stat } & p \text {-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 \end{array}   \begin{array}{rrrrrrr}  & \text { Coefficients } & \text { Standard Enor } & \text { t Stat } & p \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Invoices } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \end{array}       -Referring to Table 13-12, the 90% confidence interval for the average change in the amount of time needed as a result of processing one additional invoice is A)  narrower than [0.0109, 0.0143]. B)  wider than [0.0109, 0.0143]. C)  narrower than [0.1492, 0.6555]. D)  wider than [0.1492, 0.6555].  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 c }   { \text { Regression } \text { Statistics } } \\ \hline \text { Multiple R } & 0.9947 \\ \text { R Square } & 0.8924 \\ \text { Adjusted R Square } & 0.8886 \\ \text { Standard Error } & 0.3342 \\ \text { Observations } & 30 \\ \hline \end{array}   \begin{array}{l} \text { ANOVA }\\ \begin{array} { l c c c c c }  & d f & \text { SS } & \text { MS } & F & \text { Significance } F \\ \text { Regression } & 1 & 25.9438&25 .9438 & 232.2200&4 .3946 \mathrm { E } - 15 \\ \text { Residual } & 28 & 3.12820 .1117 & \\ \text { Total } & 29 & 29.072 \\ \hline \end{array} \end{array}   \begin{array} { l r r r r r r }  \hline & \text { Coefficients } & \text { Standard Enror } & t \text { Stat } & p \text {-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 \end{array}   \begin{array}{rrrrrrr}  & \text { Coefficients } & \text { Standard Enor } & \text { t Stat } & p \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Invoices } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \end{array}       -Referring to Table 13-12, the 90% confidence interval for the average change in the amount of time needed as a result of processing one additional invoice is A)  narrower than [0.0109, 0.0143]. B)  wider than [0.0109, 0.0143]. C)  narrower than [0.1492, 0.6555]. D)  wider than [0.1492, 0.6555].
-Referring to Table 13-12, the 90% confidence interval for the average change in the amount of time needed as a result of processing one additional invoice is

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Definitions:

Cochlear Implant

A medical device that bypasses damaged structures in the inner ear and directly stimulates the auditory nerve, allowing individuals with severe to profound hearing loss to perceive sound.

Auditory Nerve

The cranial nerve that transmits sound information from the cochlea of the inner ear to the brain, essential for hearing.

Basilar Membrane

A crucial structure in the cochlea of the inner ear, responsible for translating sound vibrations into neural signals by the movement of its hair cells.

Basilar Membrane

A core component of the cochlea in the inner ear, playing a critical role in the sense of hearing by vibrating in response to sound.

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