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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, the null hypothesis H₀: = β₁ = β₂ = β₃ = 0 implies that percentage of students passing the proficiency test is not affected by some of the explanatory variables.
-Referring to Table 14-15, the null hypothesis H₀: = β₁ = β₂ = β₃ = 0 implies that percentage of students passing the proficiency test is not affected by some of the explanatory variables.


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