Logistic regression is used to obtain the odds ratio in the presence of more than one explanatory variable. This procedure is quite similar to multiple linear regression, with the only exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage of performing logistic regression is to avoid the effects of confounders by analyzing the association of all the variables together. In this article, we explain how to perform a logistic regression using practical examples. After defining the technique, the assumptions that need to be checked are explained, along with the process of checking them using the R software.