Question
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
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. Let Y = % Passing as the dependent variable, X1 = % Attendance, X2 = Salaries and X3 = Spending. The coefficient of multiple determination (R 2 j ) of each of the 3 predictors with all the other remaining predictors are, respectively, 0.0338, 0.4669, and 0.4743. 12 The output from the best-subset regressions is given below: Model Variables Cp k R Square Adjusted R Square Std. Error 1 X1 3.05 2 0.6024 0.5936 10.5787 2 X1X2 3.66 3 0.6145 0.5970 10.5350 3 X1X2X3 4.00 4 0.6288 0.6029 10.4570 4 X1X3 2.00 3 0.6288 0.6119 10.3375 5 X2 67.35 2 0.0474 0.0262 16.3755 6 X2X3 64.30 3 0.0910 0.0497 16.1768 7 X3 62.33 2 0.0907 0.0705 15.9984 Following is the residual plot for % Attendance: Following is the output of several multiple regression models: Model (I): Coefficients Standard Error t Stat P- value Lower 95% Upper 95% Intercept -753.4225 101.1149 -7.4511 2.88E-09 -957.3401 -549.5050 % Attendance 8.5014 1.0771 7.8929 6.73E-10 6.3292 10.6735 Salary 6.85E-07 0.0006 0.0011 0.9991 -0.0013 0.0013 Spending 0.0060 0.0046 1.2879 0.2047 -0.0034 0.0153 Model (II): Coefficients Standard Error t Stat P- value Intercept -753.4086 99.1451 -7.5991 0.0000 % Attendance 8.5014 1.0645 7.9862 0.0000 Spending 0.0060 0.0034 1.7676 0.0840 Model (III): 13 df SS MS F Significance F Regression 2 8162.9429 4081.4714 39.8708 0.0000 Residual 44 4504.1635 102.3674 Total 46 12667.1064 Coefficients Standard Error t Stat P-value Intercept 6672.8367 3267.7349 2.0420 0.0472 % Attendance -150.5694 69.9519 -2.1525 0.0369 % Attendance Squared 0.8532 0.3743 2.2792 0.0276 34) Which of the following predictors should first be dropped to remove collinearity? 34) A) X1 B) X3 C) X2 D) None of the above
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