What is logistic regression used for?

Logistic regression is specifically designed to model the relationship between a binary dependent variable and one or more independent variables, which can be continuous or categorical. The primary purpose of logistic regression is to estimate the probability that a given observation falls into one of the two categories of the dependent variable. For instance, it might be used to predict whether a customer will default on a loan (yes/no) or whether a patient has a particular disease (positive/negative).

In logistic regression, the outcome variable follows a Bernoulli distribution, and the model applies the logistic function to ensure that the predicted probabilities are constrained between 0 and 1. This function allows for the interpretation of regression coefficients in terms of odds ratios, providing insights into how changes in predictor variables influence the likelihood of the outcome occurring.

While predicting continuous outcomes or analyzing multiple linear relationships is relevant in different types of regression models, these are not the focus of logistic regression. Additionally, data visualization, while important in statistical analysis, is not the purpose of logistic regression, which is fundamentally a predictive modeling technique.