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

question 176

True/False

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, you can conclude definitively that mean teacher salary individually has no impact on the mean percentage of students passing the proficiency test, taking into account the effect of that instructional spending per pupil, at a 10% level of significance based solely on but not actually computing the 90% confidence interval estimate for  \beta _ { 1 }  .

 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, you can conclude definitively that mean teacher
salary individually has no impact on the mean percentage of students passing the proficiency test,
taking into account the effect of that instructional spending per pupil, at a 10% level of
significance based solely on but not actually computing the 90% confidence interval estimate for β1\beta _ { 1 } .


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