IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

A logistic regression model is specifically designed to estimate binary outcome variables and their probabilities. This type of model is commonly used when the dependent variable is categorical with two possible outcomes, such as success/failure, yes/no, or win/lose.

Logistic regression works by modeling the probability that a given input point belongs to a certain category (e.g., the "success" class) using a logistic function. The output is interpreted as the probability of the occurrence of an event based on one or more predictor variables. This is crucial in various fields such as medicine for predicting the presence or absence of a disease, in marketing for estimating whether a customer will purchase a product, or in social sciences for determining outcomes like voting behavior.

The other types of outcomes listed in other options do not align with the purpose of logistic regression. For instance, bivariate continuous outcomes refer to two continuous variables, which would typically be analyzed using linear regression rather than logistic regression. Multiple category outcomes involve more than two classifications and would require models like multinomial logistic regression or other classification techniques. Lastly, multivariate outcomes with interactions deal with multiple dependent variables, which are beyond the scope of standard logistic regression.

Thus, the logistic regression model's strength lies in its ability to effectively