  Logistic regression is an important statistical tool for assessing the
probability of an outcome based upon some predictive variables. Standard
methods can only deal with precisely known data, however many datasets have
uncertainties which traditional methods either reduce to a single point or
completely disregarded. In this paper we show that it is possible to include
these uncertainties by considering an imprecise logistic regression model using
the set of possible models that can be obtained from values from within the
intervals. This has the advantage of clearly expressing the epistemic
uncertainty removed by traditional methods.
