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Instruction 13.22 the Education Department's Regional Executive Officer Wanted to Predict the Predict

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Instruction 13.22
The education department's regional executive officer wanted to predict the percentage of students passing a Grade 6 proficiency test. She obtained the data on percentage of students passing the proficiency test (% Passing), daily average of the percentage of students attending class (% Attendance), average 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:
Instruction 13.22 The education department's regional executive officer wanted to predict the percentage of students passing a Grade 6 proficiency test. She obtained the data on percentage of students passing the proficiency test (% Passing), daily average of the percentage of students attending class (% Attendance), average 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<sub>1</sub> = % Attendance, X<sub>2 </sub>= Salaries and X<sub>3 </sub>= Spending:    -Referring to Instruction 13.22,the alternative hypothesis H<sub>1</sub>: At least one of β<sub>j</sub> ≠ 0 for j = 1,2,3 implies that percentage of students passing the proficiency test is affected by all of the explanatory variables.
-Referring to Instruction 13.22,the alternative hypothesis H1: At least one of βj ≠ 0 for j = 1,2,3 implies that percentage of students passing the proficiency test is affected by all of the explanatory variables.


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