Logistic regression is a statistical model that is used to predict the likelihood of a binary outcome based on one or more predictor variables. It is commonly used in a variety of fields, such as medicine, economics, and marketing, to analyze and make predictions about categorical data. In logistic regression, the dependent variable is binary (i.e., it can take on two values), and the independent variables can be either continuous or categorical. The model estimates the probability of the dependent variable being in one of the two categories based on the values of the predictor variables. The output of logistic regression is a probability score between 0 and 1, which can be converted into a binary outcome using a specific threshold value. This allows researchers to make predictions about the likelihood of an event occurring based on the values of the predictor variables. Logistic regression is a powerful and widely used statistical technique for analyzing and predicting binary outcomes. It is relatively simple to implement and interpret, making it a popular choice for many researchers in various fields.