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Aside from looking at how high or how low the class standings are, Prof. Juan is also inte knowing if student performance varies significantly. This is not necessarily a bad indicator variation in student performance may reflect the discriminatory ability of course assessment past (face-to-face set-up), it is common to encounter some extreme class standings and disp the scores. Prof. Juan thinks this is not the case now and that the variance of class standing than the usual, 100 squared points. Below is the R Commander output to help clarify Pro hypothesis. R COMMANDE R OUTRUT

Aside from looking at how high or how low the class standings are, Prof. Juan is also inte knowing if student performance varies significantly. This is not necessarily a bad indicator variation in student performance may reflect the discriminatory ability of course assessment past (face-to-face set-up), it is common to encounter some extreme class standings and disp the scores. Prof. Juan thinks this is not the case now and that the variance of class standing than the usual, 100 squared points. Below is the R Commander output to help clarify Pro hypothesis. R COMMANDE R OUTRUT

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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Aside from looking at how high or how low the class standings are, Prof. Juan is also interested in knowing if student performance varies significantly. This is not necessarily a bad indicator. In fact, variation in student performance may reflect the discriminatory ability of course assessments. In the past (face-to-face set-up), it is common to encounter some extreme class standings and disparities in the scores. Prof. Juan thinks this is not the case now and that the variance of class standings is less than the usual, 100 squared points. Below is the R Commander output to help clarify Prof. Juan's hypothesis. R COMMANDER OUTPUT One sample Chi-squared test for variance data: CLASS STDG X-squared = alternative hypothesis: true variance is less than 100 7.7585, df = 49, p-value = 2.095e-12
Transcribed Image Text:Aside from looking at how high or how low the class standings are, Prof. Juan is also interested in knowing if student performance varies significantly. This is not necessarily a bad indicator. In fact, variation in student performance may reflect the discriminatory ability of course assessments. In the past (face-to-face set-up), it is common to encounter some extreme class standings and disparities in the scores. Prof. Juan thinks this is not the case now and that the variance of class standings is less than the usual, 100 squared points. Below is the R Commander output to help clarify Prof. Juan's hypothesis. R COMMANDER OUTPUT One sample Chi-squared test for variance data: CLASS STDG X-squared = alternative hypothesis: true variance is less than 100 7.7585, df = 49, p-value = 2.095e-12
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