Stata FAQ
How do I interpret odds ratios in logistic regression?

You may also want to check out, FAQ: How do I use odds ratio to interpret logistic regression?, on our General FAQ page.

Introduction

Let's begin with probability.  Let's say that the probability of success is .8, thus

p = .8

Then the probability of failure is

q = 1 - p = .2

The odds of success are defined as

odds(success) = p/q = .8/.2 = 4,

that is, the odds of success are 4 to 1. The odds of failure would be

odds(failure) = q/p = .2/.8 = .25.

This looks a little strange but it is really saying that the odds of failure are 1 to 4.  The odds of success and the odds of failure are just reciprocals of one another, i.e., 1/4 = .25 and 1/.25 = 4.  Next, we will add another variable to the equation so that we can compute an odds ratio.

Another example

This example is adapted from Pedhazur (1997).  Suppose that seven out of 10 males are admitted to an engineering school while three of 10 females are admitted. The probabilities for admitting a male are,

p = 7/10 = .7       q = 1 - .7 = .3

Here are the same probabilities for females,

p = 3/10 = .3       q = 1 - .3 = .7

Now we can use the probabilities to compute the admission odds for both males and females,

odds(male) = .7/.3 = 2.33333
odds(female) = .3/.7 = .42857

Next, we compute the odds ratio for admission,

OR = 2.3333/.42857 = 5.44

Thus, for a male, the odds of being admitted are 5.44 times as large than the odds for a female being admitted.

Logistic regression in Stata

Here are the Stata logistic regression commands and output for the example above.  In this example admit is coded 1 for yes and 0 for no and gender is coded 1 for male and 0 for female.  In Stata, the logistic command produces results in terms of odds ratios while logit produces results in terms of coefficients.

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.

About logits

There is a direct relationship between the coefficients produced by logit and the odds ratios produced by logistic.  First, let's define what is meant by a logit:  A logit is defined as the log base e (log) of the odds,

[1]     logit(p) = log(odds) = log(p/q)

Logistic regression is in reality ordinary regression using the logit as the response variable,

[2]     logit(p) = a + bX
or
[3]     log(p/q) = a + bX

This means that the coefficients in logistic regression are in terms of the log odds, that is, the coefficient 1.694596 implies that a one unit change in gender results in a 1.694596 unit change in the log of the odds.  Equation [3] can be expressed in odds by getting rid of the log.  This is done by taking e to the power for both sides of the equation.

[4]     p/q = e^{a + bX}

The end result of all the mathematical manipulations is that the odds ratio can be computed by raising e to the power of the logistic coefficient,

[5]     OR = e^b = e^1.694596 = 5.44