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The following two sets of Excel output use are from the same data set as described above, but the first set of output is for the set of 35 CEO’s that earn the lowest total compensation and the second set of output is for a set of 40 CEO’s that earn the highest total compensation. a) What is heteroscedasticity? b) Why is heteroscedasticity a problem? c) Based on a comparison of the two sets of output, does it appear that there is heteroscedasticity in the data set? Explain.

The following two sets of Excel output use are from the same data set as described above, but the first set of output is for the set of 35 CEO’s that earn the lowest total compensation and the second set of output is for a set of 40 CEO’s that earn the highest total compensation. a) What is heteroscedasticity? b) Why is heteroscedasticity a problem? c) Based on a comparison of the two sets of output, does it appear that there is heteroscedasticity in the data set? Explain.

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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3) The following two sets of Excel output use are from the same data set as described above, but the first set of output is for the set of 35 CEO’s that earn the lowest total compensation and the second set of output is for a set of 40 CEO’s that earn the highest total compensation. a) What is heteroscedasticity? b) Why is heteroscedasticity a problem? c) Based on a comparison of the two sets of output, does it appear that there is heteroscedasticity in the data set? Explain.
Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.348 0.121 0.004 479 Observations 35 ANOVA df SS MS Significance F Regression Residual 4 948703 237176 1.032 0.407 30 6893142 229771 Total 34 7841846 門TTIITT Coefficients Standard Error t Stat Intercept EMPL P-value 0.002 Lower 95% Upper 95% 732.4 1857.4 550.9 3.37 2982.4 -2.0 3.9 -0.52 0.605 -10.0 5.9 МKTVAL EPSIN 103.4 78.9 1.31 0.200 -57.7 264.6 -48.6 25.2 -1.92 0.064 -100.2 3.0 YEAR DUM 5.2 168.7 0.03 0.976 -339.3 349.6 Multiple R R Square Adjusted R Square Standard Error Observations 0.450 0.202 0.111 1521 40 ANOVA df SS MS 5129072 F Significance F Regression Residual Total 4 20516289 2.218 0.0871 35 80948642 2312818 39 101464930 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% 7950.1 Intercept EMPL MKTVAL EPSIN YEAR DUM 4047.0 1922.6 2.10 0.043 144.0 11.4 176.1 6.7 1.70 0.099 -2.2 -307.1 25.0 238.0 0.74 0.464 659.3 -365.9 153.7 -2.38 0.70 0.023 -677.9 -54.0 1387.2 356.5 507.7 0.487 -674.1
Transcribed Image Text:Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.348 0.121 0.004 479 Observations 35 ANOVA df SS MS Significance F Regression Residual 4 948703 237176 1.032 0.407 30 6893142 229771 Total 34 7841846 門TTIITT Coefficients Standard Error t Stat Intercept EMPL P-value 0.002 Lower 95% Upper 95% 732.4 1857.4 550.9 3.37 2982.4 -2.0 3.9 -0.52 0.605 -10.0 5.9 МKTVAL EPSIN 103.4 78.9 1.31 0.200 -57.7 264.6 -48.6 25.2 -1.92 0.064 -100.2 3.0 YEAR DUM 5.2 168.7 0.03 0.976 -339.3 349.6 Multiple R R Square Adjusted R Square Standard Error Observations 0.450 0.202 0.111 1521 40 ANOVA df SS MS 5129072 F Significance F Regression Residual Total 4 20516289 2.218 0.0871 35 80948642 2312818 39 101464930 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% 7950.1 Intercept EMPL MKTVAL EPSIN YEAR DUM 4047.0 1922.6 2.10 0.043 144.0 11.4 176.1 6.7 1.70 0.099 -2.2 -307.1 25.0 238.0 0.74 0.464 659.3 -365.9 153.7 -2.38 0.70 0.023 -677.9 -54.0 1387.2 356.5 507.7 0.487 -674.1
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