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

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TABLE 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), 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, X₁ = % Attendance, X₂= Salaries and X₃= Spending:
TABLE 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), 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, X₁ = % Attendance, X₂= Salaries and X₃= Spending:    -Referring to Table 14-15, you can conclude that instructional spending per pupil individually 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 1% level of significance based solely on the 95% confidence interval estimate for β₃.
-Referring to Table 14-15, you can conclude that instructional spending per pupil individually 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 1% level of significance based solely on the 95% confidence interval estimate for β₃.


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