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TABLE 14-16
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 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:
ANOVA
-Referring to Table 14-16, the alternative hypothesis H1 : At least one of þj × 0 for j = 1, 2, 3 implies that percentage of students passing the proficiency test is affected by all of the explanatory variables.
Q3: Referring to Table 14-15, the fitted model
Q16: Referring to Table 16-15, what are the
Q20: Referring to Table 14-6, what is the
Q23: The consumer price index is a Paasche
Q29: Referring to Table 12-15, the rank given
Q57: Referring to Table 12-9, the value of
Q68: The_ (larger/smaller) the value of the Variance
Q127: Referring to Table 14-16, what are the
Q162: Referring to Table 13-12, what are the
Q222: Referring to Table 14-4, which of the