In which scenario would logistic regression be most appropriate?

In which scenario would logistic regression be most appropriate?

• A. The dependent variable is continuous.
• B. The dependent variable is categorical.
• C. There are no error terms.
• D. You have more than one dependent variable.

Answer: (B)

Logistic regression is used when the outcome is categorical, especially binary. It predicts the probability of one category by modeling the log odds of the event as a linear combination of the predictors and then applying the logistic function to keep the predicted probabilities between 0 and 1. This fits because you’re aiming to classify cases rather than predict a numeric score. If the outcome were continuous, linear regression would be the default approach. The idea of having no error terms doesn’t align with regression modeling, since all such models account for unexplained variance. If there are multiple dependent variables, you’d typically use a multivariate approach rather than standard logistic regression (though there are extensions for more than two outcome categories). So the scenario with a categorical dependent variable is the one where logistic regression is most appropriate.

Logistic regression is used when the outcome is categorical, especially binary. It predicts the probability of one category by modeling the log odds of the event as a linear combination of the predictors and then applying the logistic function to keep the predicted probabilities between 0 and 1. This fits because you’re aiming to classify cases rather than predict a numeric score. If the outcome were continuous, linear regression would be the default approach. The idea of having no error terms doesn’t align with regression modeling, since all such models account for unexplained variance. If there are multiple dependent variables, you’d typically use a multivariate approach rather than standard logistic regression (though there are extensions for more than two outcome categories). So the scenario with a categorical dependent variable is the one where logistic regression is most appropriate.