输入的命令如下:
logit a1 gender age edu hedu labor outscale nfexpscale party risk nfscale xmscale land animal
a1指的是某种选择是否发生(0,1),剩下的13个变量是自变量(X)
结果如下:
Logistic regression Number of obs = 289
LR chi2(13) = 35.16
Prob > chi2 = 0.0008
Log likelihood = -150.95043 Pseudo R2 = 0.1043
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a1 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
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gender | -.3556981 .5657748 -0.63 0.530 -1.464596 .7532002
age | -.0304586 .0164084 -1.86 0.063 -.0626185 .0017014
edu | .0186853 .0541304 0.35 0.730 -.0874084 .1247789
hedu | .0078582 .0597381 0.13 0.895 -.1092264 .1249427
labor | .1651529 .1705933 0.97 0.333 -.1692038 .4995096
outscale | -.0135558 .006904 -1.96 0.050 -.0270873 -.0000242
nfexpscale | .0095279 .0070563 1.35 0.177 -.0043022 .0233581
party | .0111242 .3904193 0.03 0.977 -.7540837 .776332
risk | .0013763 .0010541 1.31 0.192 -.0006898 .0034424
nfscale | -.0042052 .0041083 -1.02 0.306 -.0122574 .003847
xmscale | .0024092 .008112 0.30 0.766 -.0134901 .0183085
land | .0215601 .0336563 0.64 0.522 -.0444051 .0875252
animal | .0936201 .0352157 2.66 0.008 .0245986 .1626416
_cons | 1.878101 1.346536 1.39 0.163 -.7610622 4.517264
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