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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 0.959 R Square 0.920 Adjusted R Square Standard Error 0.898 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 tandard Erroi 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 Gender 1.950 6.306 0.309 0.766 (12.961) 16.861 (12.961) 16.861

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 0.959 R Square 0.920 Adjusted R Square Standard Error 0.898 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 tandard Erroi 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 Gender 1.950 6.306 0.309 0.766 (12.961) 16.861 (12.961) 16.861

Linear Algebra: A Modern Introduction
Linear Algebra: A Modern Introduction
4th Edition
ISBN:9781285463247
Author:David Poole
Publisher:David Poole
Chapter7: Distance And Approximation
Section7.3: Least Squares Approximation
Problem 31EQ
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wrie down the estimated regression equation?

 

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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