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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 should be rejected at a 5% level of significance when testing whether daily average of the percentage of students attending class has any effect on percentage of students passing the proficiency test. 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 should be rejected at a 5% level of significance when testing whether daily average of the percentage of students attending class has any effect on percentage of students passing the proficiency test.
-Referring to Table 14-15, the null hypothesis should be rejected at a 5% level of significance when testing whether daily average of the percentage of students attending class has any effect on percentage of students passing the proficiency test.

Identify and differentiate between emotions, mood, and affect.
Understand the concept of positive and negative affects and their examples.
Recognize the short-lived, intense nature of emotions as compared to mood.
Explain the concepts of emotional intelligence and emotional labor.

Definitions:

GDP Deflator

A standard for assessing the price rates of all freshly made, domestically sourced, final goods and services in an economic environment.

Real GDP

Gross Domestic Product adjusted for inflation, reflecting the value of all goods and services produced by an economy in real terms.

Nominal GDP

The total economic value of all the final products and services generated within a nation's borders in a specified timeframe, calculated without any modification for inflation.

Imports

Goods or services brought into one country from another for sale or exchange.

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