A researcher ran a number of regressions involving the variables as described below. The regression results are printed in page 6 female: 1 if female; O if male age: in year aho: average hourly earings colerad: 1 if worker has a bachelor's degree: 0 if worker has a high school degree 1. As a first approximation she regressed average hourly earnings (abe) on age only. We do not need a theory to justify this because common sense tells us that age does not depend on average hourly earnings. She called this model 1. Using the regression results in page 6 report the estimated model 1 in the space below: 2. How reliable is model -1 considering that hourly earning does not depend on age alone? Education and gender affects earnings as well. How does ignoring these variables in model -1 affect her estimates? 3. Based on your thoughts as above what would be an appropriate model. Use results in page -6 to report the estimated model below. 4. Test the hypothesis that college education and gender are jointly significant determinants of average hourly earnings at a 1% level of significance. 5. What is the difference in average earning of a male from a female? Is it significant? Explain your answer. 6. Is the difference in average earning of a college graduate significantly different from that of a high- school graduate? regrensahe celgrad female age Source de Model tiendal 3304.2003 327794.7601 375415.221 595 62.621300 Number of ow PC 3.5995) Pro ured Aduared Root SE 5999 443.85 0.0000 0.1017 0.1813 7.9134 Total 458799.501598 76.49200 ahe Goet. 1959 Conf. Intervall onload female age cons 6.80443 -3.020545 -4447906 1.643414 2066659 2009872 0353069 1.061735 32.92 -14.45 12. ED 1.55 0.000 0.000 0.000 0.122 6.29929 -3.305 3755761 -437969 7.209569 -2.610855 5140040 3.724796 Tess wheelgrad Eemale SOURCE ES P MS Model Residual 73415.9234 236722.9617 365353.5725996 64.266442 Washer of che 3999 2, 5996) - 571.40 ProDY 0.0000 R- nred 0.1601 Ady R-red-0.1558 Root MS 3.0168 Total 458799.5015993 76.4920809 ahe Coet. Sud EEE t Piti 1955 coaf. Intervall colgad tele con 6.744492 -3.058649 14.06865 .2093107 -2116956 .1609106 33.22 -10.45 93.46 0.000 0.000 0.000 6.334168 -3.473640 14.5534 1.1 54017 -2.60369 15.10389 regress the colgrad age SS JP Source 5W Model 2 70302.6937 366.6085996 35151.4660 61.7926297 Number of 595 2.5996) - 542.53 Teob -0.0000 -squared 0.1532 Ad quared 0.15 E NE -2.6494 Total 458799.501 599876.92080 he Coef Sud. L. 1314 95 Cont. tateral cara CECESOE 640537 .65 21761 -3561560 20.75 12.55 03591 1.016119 0.000 0.000 0.94 5.996961 1017795 -1.10024 . 1235726 2.458734 Dahe age But SS 3.50-130.13 2.000 Model 905. 1054. 449745.1997 74.90030 - red .016 ME Total he 9. E. Cet utall 23 . 1.99 6.16 16011 1.000 0.000 con 6.170677 A researcher ran a number of regressions involving the variables as described below. The regression results are printed in page 6 female: 1 if female; O if male age: in year aho: average hourly earings colerad: 1 if worker has a bachelor's degree: 0 if worker has a high school degree 1. As a first approximation she regressed average hourly earnings (abe) on age only. We do not need a theory to justify this because common sense tells us that age does not depend on average hourly earnings. She called this model 1. Using the regression results in page 6 report the estimated model 1 in the space below: 2. How reliable is model -1 considering that hourly earning does not depend on age alone? Education and gender affects earnings as well. How does ignoring these variables in model -1 affect her estimates? 3. Based on your thoughts as above what would be an appropriate model. Use results in page -6 to report the estimated model below. 4. Test the hypothesis that college education and gender are jointly significant determinants of average hourly earnings at a 1% level of significance. 5. What is the difference in average earning of a male from a female? Is it significant? Explain your answer. 6. Is the difference in average earning of a college graduate significantly different from that of a high- school graduate? regrensahe celgrad female age Source de Model tiendal 3304.2003 327794.7601 375415.221 595 62.621300 Number of ow PC 3.5995) Pro ured Aduared Root SE 5999 443.85 0.0000 0.1017 0.1813 7.9134 Total 458799.501598 76.49200 ahe Goet. 1959 Conf. Intervall onload female age cons 6.80443 -3.020545 -4447906 1.643414 2066659 2009872 0353069 1.061735 32.92 -14.45 12. ED 1.55 0.000 0.000 0.000 0.122 6.29929 -3.305 3755761 -437969 7.209569 -2.610855 5140040 3.724796 Tess wheelgrad Eemale SOURCE ES P MS Model Residual 73415.9234 236722.9617 365353.5725996 64.266442 Washer of che 3999 2, 5996) - 571.40 ProDY 0.0000 R- nred 0.1601 Ady R-red-0.1558 Root MS 3.0168 Total 458799.5015993 76.4920809 ahe Coet. Sud EEE t Piti 1955 coaf. Intervall colgad tele con 6.744492 -3.058649 14.06865 .2093107 -2116956 .1609106 33.22 -10.45 93.46 0.000 0.000 0.000 6.334168 -3.473640 14.5534 1.1 54017 -2.60369 15.10389 regress the colgrad age SS JP Source 5W Model 2 70302.6937 366.6085996 35151.4660 61.7926297 Number of 595 2.5996) - 542.53 Teob -0.0000 -squared 0.1532 Ad quared 0.15 E NE -2.6494 Total 458799.501 599876.92080 he Coef Sud. L. 1314 95 Cont. tateral cara CECESOE 640537 .65 21761 -3561560 20.75 12.55 03591 1.016119 0.000 0.000 0.94 5.996961 1017795 -1.10024 . 1235726 2.458734 Dahe age But SS 3.50-130.13 2.000 Model 905. 1054. 449745.1997 74.90030 - red .016 ME Total he 9. E. Cet utall 23 . 1.99 6.16 16011 1.000 0.000 con 6.170677
