TABLE 14-16
the Superintendent of a School District Wanted

question 179

Multiple Choice

TABLE 14-16
The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test. She obtained the data on percentage of students passing the proficiency test (% Passing) , daily average of the percentage of students attending class (% Attendance) , average teacher salary in dollars (Salaries) , and instructional spending per pupil in dollars (Spending) of 47 schools in the state.
Following is the multiple regression output with Y = % Passing as the dependent variable, X₁ = % Attendance, X₂ = Salaries and
X₃ = Spending:
$\begin{array}{lr}\hline\text {Regression Statistics } \\\hline \text {Multiple R }& 0.7930 \\\text {R Square} & 0.6288 \\\text {Adjusted R Square} & 0.6029 \\\text {Standard Error }& 10.4570 \\\text {Observations }& 47 \\\hline\end{array}$


ANOVA
$\begin{array}{lccccc}\hline &\text { d f } &\text { SS }& \text { MS }& \text { F } & \text { Significance F} \\\hline \text { Regression }& 3 & 7965.08 & 2655.03 & 24.2802 & 2.3853 \mathrm{E}-09 \\\text { Residual} & 43 & 4702.02 & 109.35 & & \\\text { Total }& 46 & 12667.11 & & & \\\hline\end{array}$

$\begin{array}{lrrrrrr}\hline &\text { Coeffs} & \text { Stnd Err} &\text { t Stat} &\text { p -value }&\text { Lower 95\%} \text { Upper 95\%} \\\hline\text { Intercept }& -753.4225 & 101.1149 & -7.4511 & 2.88 \mathrm{E}-09 & -957.3401 & -549.5050 \\\%\text { Attend }& 8.5014 & 1.0771 & 7.8929 & 6.73 \mathrm{E}-10 & 6.3292 & 10.6735 \\\text { Salary} & 6.85 \mathrm{E}-07 & 0.0006 & 0.0011 & 0.9991 & -0.0013 & 0.0013 \\\text { Spending }& 0.0060 & 0.0046 & 1.2879 & 0.2047 & -0.0034 & 0.0153 \\\hline\end{array}$

-Referring to Table 14-16, which of the following is the correct null hypothesis to determine whether there is a significant relationship between percentage of students passing the proficiency test and the entire set of explanatory variables?

• A) \( H_{0}: \beta_{0}=\beta_{1}=\beta_{2}=\beta_{3}=0 \)
• B) \( H_{0}: \beta_{1}=\beta_{2}=\beta_{3} \neq 0 \)
• C) \( H_{0}: \beta_{1}=\beta_{2}=\beta_{3}=0 \)
• D) \( H_{0}: \beta_{0}=\beta_{1}=\beta_{2}=\beta_{3} \neq 0 \)

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Definitions:

Direct Materials Cost

The cost of raw materials that are directly traceable to the production of a specific product or service.

Manufacturing Overhead

All manufacturing costs other than those accounted for as direct labor and direct materials. Includes indirect costs such as maintenance, utilities, and rent in the production process.

Indirect Materials

Materials used in the production process that cannot be directly linked to specific products, such as lubricants and cleaning supplies.

Direct Materials Cost

The cost of raw materials and components used directly in the manufacturing of a product.