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

Question 175

True/False

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, you can conclude that instructional spending per pupil has no impact on the mean percentage of students passing the proficiency test, taking into account the effect of all the other independent variables, at a 5% level of significance using the 95% confidence interval estimate for  \beta _ { 3 }  .

 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, you can conclude that instructional spending per
pupil has no impact on the mean percentage of students passing the proficiency test, taking into
account the effect of all the other independent variables, at a 5% level of significance using the
95% confidence interval estimate for β3\beta _ { 3 } .

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