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SCENARIO 14-15
the Superintendent of a School District Wanted to Predict

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SCENARIO 14-15
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), mean teacher salary in thousands of dollars (Salaries), and instructional spending per
pupil in thousands of dollars (Spending) of 47 schools in the state. Following is the multiple regression output with Y=%Y = \% Passing as the dependent variable, X1=X _ { 1 } =
Salaries and X2=X _ { 2 } = Spending:

 Regression Statistics  Multiple R 0.4276 R Square 0.1828 Adjusted R Square 0.1457 Standard Error 5.7351 Observations 47\begin{array}{lr}\hline {\text { Regression Statistics }} \\\hline \text { Multiple R } & 0.4276 \\\text { R Square } & 0.1828 \\\text { Adjusted R Square } & 0.1457 \\\text { Standard Error } & 5.7351 \\\text { Observations } & 47 \\\hline\end{array}

ANOVA
 SCENARIO 14-15 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), mean teacher salary in thousands of dollars (Salaries), and instructional spending per pupil in thousands of dollars (Spending) of 47 schools in the state. Following is the multiple regression output with  Y = \%  Passing as the dependent variable,  X _ { 1 } =  Salaries and  X _ { 2 } =  Spending:   \begin{array}{lr} \hline {\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.4276 \\ \text { R Square } & 0.1828 \\ \text { Adjusted R Square } & 0.1457 \\ \text { Standard Error } & 5.7351 \\ \text { Observations } & 47 \\ \hline \end{array}    ANOVA     \begin{array}{lrrrrrr} \hline & \text { Coefficients } & \text { Standard Error } & \text { t Stat } & \rho \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & -72.9916 & 45.9106 & -1.5899 & 0.1190 & -165.5184 & 19.5352 \\ \text { Salary } & 2.7939 & 0.8974 & 3.1133 & 0.0032 & 0.9853 & 4.6025 \\ \text { Spending } & 0.3742 & 0.9782 & 0.3825 & 0.7039 & -1.5972 & 2.3455 \\ \hline \end{array}   -Referring to Scenario 14-15, what is the value of the test statistic when testing whether mean teacher salary has any effect on percentage of students passing the proficiency test, taking into account the effect of instructional spending per pupil?

 Coefficients  Standard Error  t Stat ρ-value  Lower 95%  Upper 95%  Intercept 72.991645.91061.58990.1190165.518419.5352 Salary 2.79390.89743.11330.00320.98534.6025 Spending 0.37420.97820.38250.70391.59722.3455\begin{array}{lrrrrrr}\hline & \text { Coefficients } & \text { Standard Error } & \text { t Stat } & \rho \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\\hline \text { Intercept } & -72.9916 & 45.9106 & -1.5899 & 0.1190 & -165.5184 & 19.5352 \\\text { Salary } & 2.7939 & 0.8974 & 3.1133 & 0.0032 & 0.9853 & 4.6025 \\\text { Spending } & 0.3742 & 0.9782 & 0.3825 & 0.7039 & -1.5972 & 2.3455 \\\hline\end{array}

-Referring to Scenario 14-15, what is the value of the test statistic when testing whether mean
teacher salary has any effect on percentage of students passing the proficiency test, taking into
account the effect of instructional spending per pupil?


Definitions:

External Oblique

The external oblique is a muscle located on the outer surface of the sides of the abdomen, involved in the movement and rotation of the trunk.

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Muscle groups found in the wall of body cavities, such as the abdominal muscles which support and contain the organs of the abdomen.

Outermost Layer

The outermost layer refers to the surface or external layer of an object or body, which is directly exposed to the environment.

Iliac Crest

The curved upper border of the ilium, the largest bone of the pelvis, important as a landmark for various surgical and clinical procedures.

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