# How to interpret the odds ratio in a logistic regression with proportion as a response variable

I have a glm model for some data with a proportion as the outcome variable as follows:

xi: glm pos_tests i.covariate_1 covariate_2 , family(binomial total_tests)


In this model, pos_tests is the number of positive laboratory results and total_tests in the number of all tests conducted.

i.covariate_1 and covariate_2 are categorical and continuous independent variables respectively.

      pos_tests  | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Icovariate_1 |   2.535989   .9193459     2.57   0.010     1.246149    5.160891
covariate_2 |    .976045   .0270064    -0.88   0.381     .9245231    1.030438


My question is how do I interpret the significant result for covariate_1.

Does it mean that the odds of all test results being positive are 2.54 time greater for those individual with covariate_1 or is there some other way of interpreting.

Thanks.

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Thanks @peter-flom, the model in stata is quite similar to R. No idea what the SAS equivalent is. I don't use it. In family(binomial total_tests) the total_tests is the number of Bernoulli trials. –  John Sep 22 '12 at 23:18