Summary.Logistic regression is a statistical modelling technique which may be applied to estimate the simultaneous effect of a set of predictors (e.g. gestational age, birthweight) on the risk of a certain outcome variable (e.g. neonatal death) which can take either one of two possible values (yes/no, alive/dead) or in the situation where one wants to estimate the effect of a particular risk factor (e.g. sex) while adjusting (correcting) for the effect of other risk factors (eg. gestational age). Since this situation often occurs both in medical or epidemiological research and in daily practice it is important to have a flexible and readily interpretable technique to predict risk of mortality and morbidity. Since the logistic regression technique is a powerful and widely applicable tool which is appearing more and more often in the epidemiological literature, a basic understanding of this technique becomes necessary for the clinical researcher. In this paper we explain logistic regression to medical researchers who do not have any particular statistical background. Part 1 covers the basic concepts. Part 2 will describe the actual representation of the basic concepts in a logistic framework.