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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 Observations 30\begin{array}{lc}\text { Regression 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\end{array}


 ANOVA \text { ANOVA }
df SS  MS F Significance F Regression 125.943825.9438232.22004.3946E15 Residual 283.12820.1117 Total 2929.072\begin{array}{lccccc} & 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 & 3.1282 & 0.1117 & & \\\text { Total } & 29 & 29.072 & & \\\hline\end{array}


 Coeffcients  StandardError 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}{lrrrrrr}\hline & \text { Coeffcients } & \text { StandardError } & 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 \\\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}{lc} \text { Regression 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 \end{array}      \text { ANOVA }   \begin{array}{lccccc}  & 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 & 3.1282 & 0.1117 & & \\ \text { Total } & 29 & 29.072 & & \\ \hline \end{array}     \begin{array}{lrrrrrr} \hline & \text { Coeffcients } & \text { StandardError } & 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 \\ \hline \end{array}       -Referring to Table 13-12, the degrees of freedom for the F test on whether the number of invoices processed affects the amount of time are A)  1, 29. B)  28, 1. C)  29, 1. D)  1, 28.  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}{lc} \text { Regression 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 \end{array}      \text { ANOVA }   \begin{array}{lccccc}  & 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 & 3.1282 & 0.1117 & & \\ \text { Total } & 29 & 29.072 & & \\ \hline \end{array}     \begin{array}{lrrrrrr} \hline & \text { Coeffcients } & \text { StandardError } & 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 \\ \hline \end{array}       -Referring to Table 13-12, the degrees of freedom for the F test on whether the number of invoices processed affects the amount of time are A)  1, 29. B)  28, 1. C)  29, 1. D)  1, 28.
-Referring to Table 13-12, the degrees of freedom for the F test on whether the number of invoices processed affects the amount of time are


Definitions:

Matriarchy

A social system in which females, particularly mothers, hold the primary positions of power and authority, in contrast to the more commonly seen patriarchal systems.

Dominant Status

A position of power, influence, or importance within a social hierarchy or structure.

Conflict Theory

A sociological perspective that views social life as a competition, focusing on the struggles between groups for limited resources.

Gender Roles

Socially constructed roles, behaviors, activities, and attributes that a society considers appropriate for men and women.

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