Logistic regression is useful method in researches which analyze categorical (two or more categories) dependent variable using independent variables. The areas where we can use logistic regression are diverse (biomedicine, insurance, etc.), and this makes logistic regression practical. This thesis explain the logistic regression from the simplest model, ie when using an independent variable we describe a dichotomous dependent variable. Then, through a multiple logistic model in which we have \(k\) independent variables by which we want to describe a dichotomous dependent variable, we come to a polynomial logistic model in which we have \(k\) independent variables by which we want to describe the categorical (\(m\) category) dependent variables. The main task of logistic regression, multiple and polynomial, is to find which independent variables best describe the dependent variable. Of course, we must keep in mind that a full logistic regression model will always better describe the dependent variable, but it is of interest to find as less as possible variables which will be good predictors. Polynomial logistic regression is performed on data of 61 patients with Crohn’s disease. Although the analysis in this thesis did not get any predictor (age, gender, sedimentation, CRP, calprotectin, and BMI) which significantly describe the degree of Crohn’s disease activity (remission, mild and severe disease), we would recommend re-analysis on a larger sample.