(1) Using w1pydu1 as a reference group:
. reg w1fer w1awer w1unemrt w1trearn w1pydu2-w1pydu6
Source SS df MS Number of obs = 165954
F( 8,165945) = 1199.92
Model 1669.98824 8 208.74853 Prob > F = 0.0000
Residual 28869.298165945 .173969074 R-squared = 0.0547
Adj R-squared = 0.0546
Total 30539.2863165953 .184023707 Root MSE = .4171
w1fer Coef. Std. Err. t P>t [95% Conf. Interval]
w1awer .3096481 .0085472 36.23 0.000 .2928958 .3264003
w1unemrt .0085848 .0003676 23.35 0.000 .0078642 .0093053
w1trearn .0106015 .0002244 47.24 0.000 .0101616 .0110414
w1pydu2 .1396929 .0035987 38.82 0.000 .1326395 .1467462
w1pydu3 .1421252 .003679 38.63 0.000 .1349143 .149336
w1pydu4 .1311907 .0037898 34.62 0.000 .1237628 .1386186
w1pydu5 .1598278 .0039954 40.00 0.000 .151997 .1676587
w1pydu6 .1868937 .0044579 41.92 0.000 .1781563 .1956311
_cons .3441855 .0039465 87.21 0.000 .3364505 .3519205
(2) Using w1pydu6 as a reference group:
. reg w1fer w1awer w1unemrt w1trearn w1pydu1-w1pydu5
Source SS df MS Number of obs = 165954
F( 8,165945) = 1005.12
Model 1411.40693 8 176.425866 Prob > F = 0.0000
Residual 29127.8793165945 .175527309 R-squared = 0.0462
Adj R-squared = 0.0462
Total 30539.2863165953 .184023707 Root MSE = .41896
w1fer Coef. Std. Err. t P>t [95% Conf. Interval]
w1awer .4148088 .0088145 47.06 0.000 .3975326 .432085
w1unemrt .0055719 .0003837 14.52 0.000 .0048199 .0063239
w1trearn .0136808 .0002119 64.56 0.000 .0132654 .0140961
w1pydu1 .0718526 .0043821 16.40 0.000 .0632639 .0804414
w1pydu2 .1209076 .0042133 28.70 0.000 .1126496 .1291655
w1pydu3 .1218828 .0042776 28.49 0.000 .1134987 .1302668
w1pydu4 .0956922 .0040945 23.37 0.000 .0876671 .1037172
w1pydu5 .1207567 .0042355 28.51 0.000 .1124552 .1290582
_cons .3568597 .004034 88.46 0.000 .3489532 .3647662
Thanks in advance.
Do Han Kim
PhD Program, Department of Public Administration and Policy
Rockefeller College of Public Affairs and Policy
SUNY at Albany
[email protected]
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