Example - Logistic Regression of Frequent Binge Drinking (yes=1, no=0)
on Gender (males=1, females=0)

Summary of Data:
 
YES n
Male 1630 7180
Female 1684 9916
Total 3314 17096

Entering Data into computer:
 
binge gender
0 0
0 0
0 1
1 1
0 1
0 0
1 0
1 1
0 0
etc..  for a total of 17096 lines

Minitab Output:

Binary Logistic Regression: binge versus gender

Link Function: Logit

Response Information
Variable  Value  Count
binge     1       3314  (Event)
          0      13782
          Total  17096
 

Logistic Regression Table
                                                Odds     95% CI
Predictor      Coef    SE Coef       Z      P  Ratio  Lower  Upper
Constant   -1.58686  0.0267449  -59.33  0.000
gender     0.361639  0.0388452    9.31  0.000   1.44   1.33   1.55
 

Log-Likelihood = -8363.805
Test that all slopes are zero: G = 86.396, DF = 1, P-Value = 0.000

* NOTE * No goodness of fit test performed.
* NOTE * The model uses all degrees of freedom.

Measures of Association:
(Between the Response Variable and Predicted Probabilities)

Pairs         Number  Percent  Summary Measures
Concordant  13418160     29.4  Somers' D              0.09
Discordant   9346200     20.5  Goodman-Kruskal Gamma  0.18
Ties        22909188     50.2  Kendall's Tau-a        0.03
Total       45673548    100.0