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input admit gender freq
1 1 7
1 0 3
0 1 3
0 0 7
end
logit admit gender [fweight=freq], nolog or
(frequency weights assumed)
Logistic regression Number of obs = 20
LR chi2(1) = 3.29
Prob > chi2 = 0.0696
Log likelihood = -12.217286 Pseudo R2 = 0.1187
------------------------------------------------------------------------------
admit | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gender | 5.444444 5.313234 1.74 0.082 .8040183 36.86729
------------------------------------------------------------------------------
/* Note: the above command is equivalent to --
logistic admit gender [weight=freq], nolog */
logit admit gender [weight=freq], nolog
(frequency weights assumed)
Logistic regression Number of obs = 20
LR chi2(1) = 3.29
Prob > chi2 = 0.0696
Log likelihood = -12.217286 Pseudo R2 = 0.1187
------------------------------------------------------------------------------
admit | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gender | 1.694596 .9759001 1.74 0.082 -.2181333 3.607325
_cons | -.8472979 .6900656 -1.23 0.220 -2.199801 .5052058
------------------------------------------------------------------------------
Note that z = 1.74 for the coefficient for
gender and for the odds ratio for gender.UCLA Researchers are invited to our Statistical Consulting Services
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