In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an essential added ingredient. The application of the product rule gives the posterior of the unknown logistic regression coefficients. The Jacobian transformation then maps the posterior of these regression coefficients to the posterior of the corresponding probability of some event and some nuisance parameters. Finally, by way of the sumrule the nuissance parameters are integrated out.