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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), mean teacher salar 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, = Salaries and X, = Spending. E Click the icon to view the multiple regression output. Determine whether the following statement is true or false: The null hypothesis Hn: B, = B, = 0 implies that percentage of students passing the proficiency test is not related to one of the explanatory variables. - X Multiple Regression Output O True O False Regression Statistics Multiple R 0.4276 RSquare 0.1828 Adjusted R Square 0.1457 Standard Error 5.7351 Observations 47 ANOVA df Significance F MS Regression 2 323.8284 161.9142 4.9227 0.0118 Residual 44 1447.2094 32.8911 Total 46 1771.0378 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -72.9916 45.9106 -1.5899 0.1190 -165.5184 19.5352 Salary 2.7939 0.8974 3.1133 0.0032 0.9853 4.6025 Spending 0.3742 0.9782 0.3825 0.7039 -1.5972 2.3455

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), mean teacher salar 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, = Salaries and X, = Spending. E Click the icon to view the multiple regression output. Determine whether the following statement is true or false: The null hypothesis Hn: B, = B, = 0 implies that percentage of students passing the proficiency test is not related to one of the explanatory variables. - X Multiple Regression Output O True O False Regression Statistics Multiple R 0.4276 RSquare 0.1828 Adjusted R Square 0.1457 Standard Error 5.7351 Observations 47 ANOVA df Significance F MS Regression 2 323.8284 161.9142 4.9227 0.0118 Residual 44 1447.2094 32.8911 Total 46 1771.0378 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -72.9916 45.9106 -1.5899 0.1190 -165.5184 19.5352 Salary 2.7939 0.8974 3.1133 0.0032 0.9853 4.6025 Spending 0.3742 0.9782 0.3825 0.7039 -1.5972 2.3455

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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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), 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, = Salaries and X, = Spending. E Click the icon to view the multiple regression output. Determine whether the following statement is true or false: The null hypothesis Hn: B, = B, = 0 implies that percentage of students passing the proficiency test is not related to one of the explanatory variables. Multiple Regression Output True False Regression Statistics Multiple R 0.4276 R Square 0.1828 Adjusted R Square 0.1457 Standard Error 5.7351 Observations 47 ANOVA df SS MS F Significance F Regression 323.8284 161.9142 4.9227 0.0118 Residual 44 1447.2094 32.8911 Total 46 1771.0378 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -72.9916 45,9106 -1.5899 0.1190 -165.5184 19.5352 Salary 2.7939 0.8974 3.1133 0.0032 0.9853 4.6025 Spending 0.3742 0.9782 0.3825 0.7039 -1.5972 2.3455
Transcribed Image Text: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), 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, = Salaries and X, = Spending. E Click the icon to view the multiple regression output. Determine whether the following statement is true or false: The null hypothesis Hn: B, = B, = 0 implies that percentage of students passing the proficiency test is not related to one of the explanatory variables. Multiple Regression Output True False Regression Statistics Multiple R 0.4276 R Square 0.1828 Adjusted R Square 0.1457 Standard Error 5.7351 Observations 47 ANOVA df SS MS F Significance F Regression 323.8284 161.9142 4.9227 0.0118 Residual 44 1447.2094 32.8911 Total 46 1771.0378 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -72.9916 45,9106 -1.5899 0.1190 -165.5184 19.5352 Salary 2.7939 0.8974 3.1133 0.0032 0.9853 4.6025 Spending 0.3742 0.9782 0.3825 0.7039 -1.5972 2.3455
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