Clyde Schechter

is the effect of age on seius different for male and female?


reg seius i.female##c.age


margins female

margins, at(age=(20(10)60))


margins female, at(age=(20(10)60))

margins, dydx(female) at(age=(20(10)60))


reg seius i.female##c.age

Source SS df MS Number of obs = 1148
F( 3, 1144) = 3.71
Model 1273.17883 3 424.392942 Prob > F = 0.0114
Residual 131023.563 1144 114.531087 R-squared = 0.0096
Adj R-squared = 0.0070
Total 132296.742 1147 115.341536 Root MSE = 10.702


seius Coef. Std. Err. t P>t [95% Conf. Interval]

1.female 1.11425 2.131621 0.52 0.601 -3.068075 5.296576
age .1177876 .0480567 2.45 0.014 .0234984 .2120768

female#c.age
1 -.0774017 .0660201 -1.17 0.241 -.2069357 .0521324

_cons 10.99175 1.55593 7.06 0.000 7.938952 14.04454







Predictive margins Number of obs = 1148
Model VCE : OLS

Expression : Linear prediction, predict()


Delta-method
Margin Std. Err. t P>t [95% Conf. Interval]

female
0 14.62316 .4349116 33.62 0.000 13.76984 15.47647
1 13.3511 .4598713 29.03 0.000 12.44882 14.25339



. margins, at(age=(20(10)60))

Predictive margins Number of obs = 1148
Model VCE : OLS

Expression : Linear prediction, predict()

1._at : age = 20

2._at : age = 30

3._at : age = 40

4._at : age = 50

5._at : age = 60


Delta-method
Margin Std. Err. t P>t [95% Conf. Interval]

_at
1 13.1427 .4792111 27.43 0.000 12.20247 14.08293
2 13.95514 .3172715 43.98 0.000 13.33264 14.57764
3 14.76759 .4378812 33.73 0.000 13.90845 15.62673
4 15.58003 .70914 21.97 0.000 14.18867 16.97139
5 16.39247 1.016897 16.12 0.000 14.39728 18.38767


margins female, at(age=(20(10)60))

Predictive margins Number of obs = 1148
Model VCE : OLS

Expression : Linear prediction, predict()

1._at : age = 20

2._at : age = 30

3._at : age = 40

4._at : age = 50

5._at : age = 60


Delta-method
Margin Std. Err. t P>t [95% Conf. Interval]

_at#female
1 0 13.3475 .6876772 19.41 0.000 11.99825 14.69675
1 1 12.91372 .662621 19.49 0.000 11.61363 14.21381
2 0 14.52538 .4378656 33.17 0.000 13.66627 15.38449
2 1 13.31758 .4603422 28.93 0.000 12.41437 14.22079
3 0 15.70325 .6102808 25.73 0.000 14.50586 16.90065
3 1 13.72144 .6281752 21.84 0.000 12.48893 14.95394
4 0 16.88113 1.007496 16.76 0.000 14.90438 18.85788
4 1 14.1253 .9935462 14.22 0.000 12.17592 16.07467
5 0 18.059 1.455866 12.40 0.000 15.20254 20.91547
5 1 14.52916 1.410498 10.30 0.000 11.7617 17.29661



. margins, dydx(female) at(age=(20(10)60))

Conditional marginal effects Number of obs = 1148
Model VCE : OLS

Expression : Linear prediction, predict()
dy/dx w.r.t. : 1.female

1._at : age = 20

2._at : age = 30

3._at : age = 40

4._at : age = 50

5._at : age = 60


Delta-method
dy/dx Std. Err. t P>t [95% Conf. Interval]

1.female
_at
1 -.4337829 .9549693 -0.45 0.650 -2.307471 1.439905
2 -1.2078 .6353276 -1.90 0.058 -2.454338 .0387385
3 -1.981816 .875812 -2.26 0.024 -3.700194 -.2634381
4 -2.755833 1.414985 -1.95 0.052 -5.53209 .0204244
5 -3.52985 2.027079 -1.74 0.082 -7.50706 .4473607

Note: dy/dx for factor levels is the discrete change from the base level.



is the effect of age on seius different for male and female?