Logistic regression is primarily used for predicting which type of outcome?

Logistic regression is primarily used for predicting which type of outcome?

• A. Continuous outcome.
• B. Ordinal outcome.
• C. Binary outcome.
• D. Time-to-event outcome.

Answer: (C)

Logistic regression is used when the outcome is binary. It aims to predict the probability that the event occurs, given a set of predictors. Because probabilities lie between 0 and 1, the model uses the logistic (logit) function to transform a linear combination of predictors into a value on the 0–1 scale. In practice, results are often interpreted in terms of how predictors shift the odds of the event occurring. If the outcome were continuous, linear regression would be appropriate; for ordered categories, ordinal regression; and for time-to-event data, survival analysis like Cox regression. This focus on predicting a yes/no type outcome is what makes logistic regression the primary approach.

Logistic regression is used when the outcome is binary. It aims to predict the probability that the event occurs, given a set of predictors. Because probabilities lie between 0 and 1, the model uses the logistic (logit) function to transform a linear combination of predictors into a value on the 0–1 scale. In practice, results are often interpreted in terms of how predictors shift the odds of the event occurring. If the outcome were continuous, linear regression would be appropriate; for ordered categories, ordinal regression; and for time-to-event data, survival analysis like Cox regression. This focus on predicting a yes/no type outcome is what makes logistic regression the primary approach.