Confidence interval for the intercept in logistic regression Some major commercial statistical packages (e.g., SPSS) do not report a CI for the intercept term in logistic regression. [Based on answer below R does provide CI for intercept] 
Why might confidence intervals for the intercept term not be included by default?
UPDATE:
Based on feedback, Confidence Intervals for odds associated with intercept term are reported in some stat. packages. And, obviously, they can be easily computed manually, knowing the standard error. Therefore, reformulated question:
What is the interpretation of intercept's CIs (for odds) in logistic regression?
 A: Well, that's not correct. You can use confint funtion in both S-plus and R to obtain C.I. for the estimated parameters. I will give an example for logistic regression in R since I don't have the S-plus:
> mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv")
> mylogit <- glm(admit ~ gre + gpa + rank, data = mydata, family = "binomial")
> confint(mylogit)
Waiting for profiling to be done...
                    2.5 %       97.5 %
(Intercept) -5.7109591680 -1.260314066
gre          0.0001715446  0.004461385
gpa          0.1415710585  1.428341503
rank        -0.8149612229 -0.315479733
>

A: I don't have access to SPSS, but I strongly suspect that it can output a confidence interval for the intercept in logistic regression.  You probably have to know how to use the underlying syntax (e.g., PASTE) to call for it.  
As to why it isn't output by default, you'd really have to contact the company.  But I would guess that they believe people aren't very interested in seeing the CI for the intercept and want to minimize the volume of statistical output.  
