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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

College Algebra
College Algebra
1st Edition
ISBN:9781938168383
Author:Jay Abramson
Publisher:Jay Abramson
Chapter4: Linear Functions
Section4.3: Fitting Linear Models To Data
Problem 24SE: Table 6 shows the year and the number ofpeople unemployed in a particular city for several years....
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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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