Key - Lab 10 Wages Regression Analysis

Key - Lab 10 Wages Regression Analysis - Lab 10 Key...

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Lab 10 Key Regression Analysis: wage versus yrsed, ex The regression equation is wage = - 4.78 + 1.46 yrsed + 0.126 ex Predictor Coef SE Coef T P Constant -4.780 2.146 -2.23 0.026 yrsed 1.4623 0.1503 9.73 0.000 ex 0.12635 0.02739 4.61 0.000 S = 8.98501 R-Sq = 11.9% R-Sq(adj) = 11.7% Analysis of Variance Source DF SS MS F P Regression 2 8233.2 4116.6 50.99 0.000 Residual Error 754 60870.8 80.7 Total 756 69104.0 Regression Analysis: wage versus yrsed, ex, fe, feex The regression equation is wage = - 4.66 + 1.49 yrsed + 0.155 ex - 1.03 fe - 0.0478 feex Predictor Coef SE Coef T P Constant -4.657 2.184 -2.13 0.033 yrsed 1.4850 0.1499 9.91 0.000 ex 0.15522 0.03771 4.12 0.000 fe -1.031 1.311 -0.79 0.432 feex -0.04778 0.05348 -0.89 0.372 S = 8.93383 R-Sq = 13.1% R-Sq(adj) = 12.7% Analysis of Variance Source DF SS MS F P Regression 4 9084.5 2271.1 28.46 0.000 Residual Error 752 60019.6 79.8 Total 756 69104.0 The estimated coefficients for fe and feex are interpreted as follows: For fe , a woman earns a starting salary (ex=0) that is $1.03 less than those of men, holding education constant. Reviewing the calculated t statistic, we would conclude that fe does not have a statistically significant effect on wage. For feex , a woman earns a raise that is about $0.05 less per hour than a man earns, holding education constant. This effect is also not statistically significant according to a t test. But, we should conduct a t test before removing these variables from our model. As an aside, we should note that the adjusted R square did increase after adding these two variables. 21 22 [ ] / 2 [9084.5 8233.2] / 2 425.65 5.33 / ( 1) 60,019.6 / 752 79.8 alc ESS ESS F RSS n K == = = −−
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This calculated F statistic will be statistically significant (greater than any of the critical values). The two variables do affect wages, or at least one does. Regression Analysis: wage versus yrsed, ex, exsq, fe, feex The regression equation is wage = - 6.47 + 1.42 yrsed + 0.493 ex - 0.00756 exsq - 1.15 fe - 0.0431 feex Predictor Coef SE Coef T P Constant -6.468 2.203 -2.94 0.003 yrsed 1.4214 0.1491 9.53 0.000 ex 0.49257 0.08883 5.55 0.000 exsq -0.007558 0.001806 -4.18 0.000 fe -1.148 1.297 -0.89 0.376 feex -0.04310 0.05292 -0.81 0.416 S = 8.83732 R-Sq = 15.1% R-Sq(adj) = 14.6% Analysis of Variance Source DF SS MS F P Regression 5 10452.2 2090.4 26.77 0.000 Residual Error 751 58651.8 78.1 Total 756 69104.0 We generated “fitted values” for wages using Minitab’s Storage option. We then plotted the fitted values. Why such a scatter? Shouldn’t we see a clear “hill
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Key - Lab 10 Wages Regression Analysis - Lab 10 Key...

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