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SCENARIO 17-8
the Superintendent of a School District Wanted to Predict

Question 169

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SCENARIO 17-8
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 mean of the percentage of students attending class (% Attendance), mean 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=%Y = \% Passing as the dependent variable, X1=%X _ { 1 } = \% Attendance, X2=X _ { 2 } = Salaries and X3=X _ { 3 } = Spending:

 Regression Statistics  Multiple R 0.7930 R Square 0.6288 Adjusted R 0.6029 Square  Standard 10.4570 Error  Observations 47\begin{array}{lr}\hline{\text { Regression Statistics }} \\\hline \text { Multiple R } & 0.7930 \\\text { R Square } & 0.6288 \\\text { Adjusted R } & 0.6029 \\\text { Square } & \\\text { Standard } & 10.4570 \\\text { Error } & \\\text { Observations } & 47 \\\hline\end{array}

ANOVA
 SCENARIO 17-8 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 mean of the percentage of students attending class (% Attendance), mean 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,  X _ { 1 } = \%  Attendance,  X _ { 2 } =  Salaries and  X _ { 3 } =  Spending:   \begin{array}{lr} \hline{\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.7930 \\ \text { R Square } & 0.6288 \\ \text { Adjusted R } & 0.6029 \\ \text { Square } & \\ \text { Standard } & 10.4570 \\ \text { Error } & \\ \text { Observations } & 47 \\ \hline \end{array}    ANOVA     \begin{array}{lrrrrrr} \hline & \text { Coefficients } & \begin{array}{c} \text { Standard } \\ \text { Error } \end{array} & \text { t Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & -753.4225 & 101.1149 & -7.4511 & 0.0000 & -957.3401 & -549.5050 \\ \text { \% Attendance } & 8.5014 & 1.0771 & 7.8929 & 0.0000 & 6.3292 & 10.6735 \\ \text { Salary } & 0.000000685 & 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 Scenario 17-8, what are the lower and upper limits of the 95% confidence interval estimate for the effect of a one dollar increase in instructional spending per pupil on the mean percentage of students passing the proficiency test?

 Coefficients  Standard  Error  t Stat  P-value  Lower 95%  Upper 95%  Intercept 753.4225101.11497.45110.0000957.3401549.5050 % Attendance 8.50141.07717.89290.00006.329210.6735 Salary 0.0000006850.00060.00110.99910.00130.0013 Spending 0.00600.00461.28790.20470.00340.0153\begin{array}{lrrrrrr}\hline & \text { Coefficients } & \begin{array}{c}\text { Standard } \\\text { Error }\end{array} & \text { t Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \\\hline \text { Intercept } & -753.4225 & 101.1149 & -7.4511 & 0.0000 & -957.3401 & -549.5050 \\\text { \% Attendance } & 8.5014 & 1.0771 & 7.8929 & 0.0000 & 6.3292 & 10.6735 \\\text { Salary } & 0.000000685 & 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 Scenario 17-8, what are the lower and upper limits of the 95% confidence interval
estimate for the effect of a one dollar increase in instructional spending per pupil on the mean
percentage of students passing the proficiency test?

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−0.0034 (%...

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