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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 some 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 some 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 some of the explanatory variables.


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Awareness

The degree to which consumers are familiar with the presence and characteristics of a product, brand, or issue.

Adoption

The act of accepting, taking up, or starting to use something new or different, such as technology or practices.

Hierarchy of Effects

A framework outlining the steps a consumer takes, beginning with awareness and progressing through knowledge, appreciation, preference, conviction, to ultimately making a purchase.

Awareness

The state or level of consciousness where individuals or groups realize the existence or importance of something.

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