Logistic regression is a statistical method used to predict the likelihood of an event happening based on certain factors. It is commonly used when the outcome is binary, like yes or no. The researchers in this article discuss how logistic regression models work and how they can be extended for more complex analyses. They found that the sample size needed for logistic regression is usually larger than for regular linear regression because the outcome is categorical. The goal of logistic regression is to create an equation that can estimate the probability of an event occurring based on independent variables. Tests like Wald's test and likelihood ratio test can be used to check the significance of the variables in the model.