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

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.