Examlex

Solved

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

question 141

Multiple Choice

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


Definitions:

Fatty Acids And Glycerol

Building blocks of fats in organisms; fatty acids are long hydrocarbon chains, while glycerol is a three-carbon molecule that binds fatty acids to form triglycerides.

Amino Acids

Organic compounds that serve as the building blocks of proteins, necessary for various bodily functions.

Monosaccharides

Simple sugars that are the most basic form of carbohydrates, consisting of one sugar molecule and serving as the building blocks for more complex sugars.

Polysaccharides

Complex carbohydrates composed of long chains of monosaccharide units bound together by glycosidic linkages, serving roles in energy storage and structural support.

Related Questions