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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 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:
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, X<sub>1</sub>= % Attendance, X<sub>2</sub>= Salaries and X<sub>3</sub>= Spending:    Note:    -Referring to Table 14-15, the null hypothesis H<sub>0</sub> : β<sub>1</sub> = β<sub>2</sub> = β<sub>3</sub> = 0 implies that percentage of students passing the proficiency test is not affected by any of the explanatory variables. Note:
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, X<sub>1</sub>= % Attendance, X<sub>2</sub>= Salaries and X<sub>3</sub>= Spending:    Note:    -Referring to Table 14-15, the null hypothesis H<sub>0</sub> : β<sub>1</sub> = β<sub>2</sub> = β<sub>3</sub> = 0 implies that percentage of students passing the proficiency test is not affected by any 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 any of the explanatory variables.


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Property Rights

Legal rights to possess, use, and dispose of assets, including real estate, intellectual property, or physical goods, crucial for the functioning of markets and economies.

Government Regulation

Rules and guidelines established by the government aimed at influencing or controlling certain activities within the society or economy.

Total Surplus

The total net gain to society from a market transaction, which is the combination of consumer surplus and producer surplus.

Consumer Surplus

The gap between the aggregate amount buyers are willing and able to spend for a good or service, versus what they actually spend.

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