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e) If a female salesperson told you that she has a 2-year college degree, what is your prediction on her annual income? Cor the

e) If a female salesperson told you that she has a 2-year college degree, what is your prediction on her annual income? Cor the

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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QUESTION 13 e) If a female salesperson told you that she has a 2-year college degree, what is your prediction on her annual income? For the toolbar, press ALT+F10 (PC) or ALT+FN+F10 (Mac).
Transcribed Image Text:QUESTION 13 e) If a female salesperson told you that she has a 2-year college degree, what is your prediction on her annual income? For the toolbar, press ALT+F10 (PC) or ALT+FN+F10 (Mac).
2. Following Question 1. The researcher thought that another variable, gender, might also affect the income level. Thus, he created a dummy variable for gender (1 for male and 0 for female) and estimated the regression model with two independent variables, SUMMARY OUTPUT Regression Statistics Multiple R R Square 0.959 0.920 Adjusted R Square 0.898 Standard Error 3.988 Observations 10.000 ANOVA df SS MS F Significance F Regression 2.000 1,286.766 643.383 40.451 0.000 Residual 7.000 111.337 15.905 Total 9.000 1,398.103 Lower 95% Upper 95% Lower 95.0% Upper 95.0% (9.301) Coefficients itandard Erro t Stat P-value 10.140. 8.222 1.233 0.257 29.581 (9.301) 29.581 Intercept Years in College 11.700 3.454 3.388 0.012 3.533 19.867 3.533 19.867 1.950 6.306 0.309 0.766 (12.961) 16.861 (12.961) 16.861 Gender
Transcribed Image Text:2. Following Question 1. The researcher thought that another variable, gender, might also affect the income level. Thus, he created a dummy variable for gender (1 for male and 0 for female) and estimated the regression model with two independent variables, SUMMARY OUTPUT Regression Statistics Multiple R R Square 0.959 0.920 Adjusted R Square 0.898 Standard Error 3.988 Observations 10.000 ANOVA df SS MS F Significance F Regression 2.000 1,286.766 643.383 40.451 0.000 Residual 7.000 111.337 15.905 Total 9.000 1,398.103 Lower 95% Upper 95% Lower 95.0% Upper 95.0% (9.301) Coefficients itandard Erro t Stat P-value 10.140. 8.222 1.233 0.257 29.581 (9.301) 29.581 Intercept Years in College 11.700 3.454 3.388 0.012 3.533 19.867 3.533 19.867 1.950 6.306 0.309 0.766 (12.961) 16.861 (12.961) 16.861 Gender
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