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 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:
Note:
-Referring to Table 14-15, what is the p-value of the test statistics when testing whether instructional spending per pupil has any effect on percentage of students passing the proficiency test?
Definitions:
Q98: Referring to Table 11-6, the null hypothesis
Q125: Referring to Table 13-10, what is the
Q126: If you wanted to find out if
Q129: Referring to Table 17-9, an <img src="https://d2lvgg3v3hfg70.cloudfront.net/TB4634/.jpg"
Q132: Referring to Table 13-3, suppose the director
Q149: Referring to Table 12-10, the null hypothesis
Q165: Referring to Table 13-1, interpret the estimate
Q170: When an additional explanatory variable is introduced
Q193: The slope (b<sub>1</sub>) represents<br>A) predicted value of
Q212: Referring to Table 14-3, the p-value for