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随便执行个数据,是没有你的问题的
. use auto
(1978 Automobile Data)
. by foreign: rreg price weight
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-> foreign = Domestic
Huber iteration 1: maximum difference in weights = .73594307
Huber iteration 2: maximum difference in weights = .29660597
Huber iteration 3: maximum difference in weights = .18585558
Huber iteration 4: maximum difference in weights = .07025816
Huber iteration 5: maximum difference in weights = .04247984
Biweight iteration 6: maximum difference in weights = .29881926
Biweight iteration 7: maximum difference in weights = .41229889
Biweight iteration 8: maximum difference in weights = .24627694
Biweight iteration 9: maximum difference in weights = .09056134
Biweight iteration 10: maximum difference in weights = .02040438
Biweight iteration 11: maximum difference in weights = .01049715
Biweight iteration 12: maximum difference in weights = .00483083
Robust regression Number of obs = 52
F( 1, 50) = 30.81
Prob > F = 0.0000
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price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
weight | .9645098 .1737746 5.55 0.000 .6154732 1.313546
_cons | 1754.39 588.7213 2.98 0.004 571.9088 2936.872
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-> foreign = Foreign
Huber iteration 1: maximum difference in weights = .21259834
Huber iteration 2: maximum difference in weights = .03056369
Biweight iteration 3: maximum difference in weights = .14265647
Biweight iteration 4: maximum difference in weights = .00192243
Robust regression Number of obs = 22
F( 1, 20) = 66.82
Prob > F = 0.0000
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price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
weight | 5.444803 .6661087 8.17 0.000 4.055325 6.834282
_cons | -6209.538 1568.174 -3.96 0.001 -9480.691 -2938.384
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