Essay from the year 2023 in the subject Economics - Statistics and Methods, grade: 2,0, University of Bayreuth, language: English, abstract: In many areas of science as well as in practice in economics, politics, etc., it is a matter of determining the probability of the occurrence of a certain event. In the field of marketing, for example, it is interesting to know which factors increase the probability of a purchase, in the field of medicine it is important to know which factors increase the risk of an illness, and in politics it will be of interest to determine the effects of certain variables on the probability of being elected. All these events can be viewed as dichotomous (binary) variables (purchase - non-purchase; disease - non-disease; election - non-election; etc.). It is precisely in these cases, when the dependent variable is dichotomous, that linear regression fails to provide a satisfactory answer. The probability for the occurrence of the event (dependent/endogenous/explained variable or regressand or also called prognosis variable) is therefore 1 minus the probability for the non-occurrence of the event. With the help of logistic regression the probabilities for the occurrence of an event can be calculated. On the one hand, this method has similarities to discriminant analysis in that it is a two-group approach. On the other hand, there are similarities to linear regression analysis, since the independent variables (exogenous/explanatory variables, regressor or predictor variables) are weighted via a regression approach (see Backhaus, p. 284). In Machine Learning and Medicine Logistic regression is widely used. Machine Learning can have a major impact in medicine since it helps analyze an enormous amount of data (See Tripepi et., 2008, p.808). This papers aim is to create a machine learning tool with logistic regression that analyzes Covid-19 Data from Mexico. In future pandemics such an algorithm could help physicians and health authorities to answer questions like: Who should be vaccinated first or who has a higher risk of a severe course of the disease? Answering these kind of questions quick and based on data could save a lot of resources and could dampen the course of the pandemic. Furthermore it simplifies difficult decisions and could also provide help for decision making in a Triage-Situation when resources are scarce.