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SCENARIO 14-15 the Superintendent of a School District Wanted to Predict the Predict

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SCENARIO 14-15
The superintendent of a school district wanted to predict the percentage of students passing a sixthgrade proficiency test. She obtained the data on percentage of students passing the proficiency test (% Passing) , mean teacher salary in thousands of dollars (Salaries) , and instructional spending per pupil in thousands of dollars (Spending) of 47 schools in the state.
Following is the multiple regression output with Y = % Passing as the dependent variable, X1 = Salaries and X 2 = Spending:
SCENARIO 14-15 The superintendent of a school district wanted to predict the percentage of students passing a sixthgrade proficiency test. She obtained the data on percentage of students passing the proficiency test (% Passing) , mean teacher salary in thousands of dollars (Salaries) , and instructional spending per pupil in thousands of 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> = Salaries and X <sub>2</sub> = Spending:     -Referring to Scenario 14-15,which of the following is a correct statement? A) 18.28% of the total variation in the percentage of students passing the proficiency test can be explained by mean teacher salary and instructional spending per pupil. B) 18.28% of the total variation in the percentage of students passing the proficiency test can be explained by mean teacher salary and instructional spending per pupil after adjusting for the number of predictors and sample size. C) 18.28% of the total variation in the percentage of students passing the proficiency test can be explained by mean teacher salary holding constant the effect of instructional spending per pupil. D) 18.28% of the total variation in the percentage of students passing the proficiency test can be explained by instructional spending per pupil holding constant the effect of mean teacher salary.
SCENARIO 14-15 The superintendent of a school district wanted to predict the percentage of students passing a sixthgrade proficiency test. She obtained the data on percentage of students passing the proficiency test (% Passing) , mean teacher salary in thousands of dollars (Salaries) , and instructional spending per pupil in thousands of 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> = Salaries and X <sub>2</sub> = Spending:     -Referring to Scenario 14-15,which of the following is a correct statement? A) 18.28% of the total variation in the percentage of students passing the proficiency test can be explained by mean teacher salary and instructional spending per pupil. B) 18.28% of the total variation in the percentage of students passing the proficiency test can be explained by mean teacher salary and instructional spending per pupil after adjusting for the number of predictors and sample size. C) 18.28% of the total variation in the percentage of students passing the proficiency test can be explained by mean teacher salary holding constant the effect of instructional spending per pupil. D) 18.28% of the total variation in the percentage of students passing the proficiency test can be explained by instructional spending per pupil holding constant the effect of mean teacher salary.
-Referring to Scenario 14-15,which of the following is a correct statement?


Definitions:

Citric Acid Cycle

A sequence of biochemical processes that aerobic life forms use to liberate energy stored by oxidizing acetyl-CoA, obtained from carbs, fats, and proteins.

Glucose Molecule

A simple sugar that is an important energy source in living organisms and a component of many carbohydrates.

Oxidize Glucose

The process of glucose reacting with oxygen to produce energy, carbon dioxide, and water, primarily through cellular respiration.

Metabolize Proteins

The process of breaking down proteins into their amino acid components, which can then be used by the body for energy or to make new proteins.

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