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

question 75

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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, the alternative hypothesis  H _ { 1 } : \text { At least one of } \beta _ { j } \neq 0 \text { for } j = 1,2  implies that percentage of students passing the proficiency test is affected by at least one of the explanatory variables.

 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, the alternative hypothesis H1: At least one of βj0 for j=1,2H _ { 1 } : \text { At least one of } \beta _ { j } \neq 0 \text { for } j = 1,2 implies that percentage of students passing the proficiency test is affected by at least one of the
explanatory variables.


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