Editing Logistic distribution (section)

=== Logistic regression ===
One of the most common applications is in [[logistic regression]], which is used for modeling [[categorical variable|categorical]] [[dependent variable]]s (e.g., yes-no choices or a choice of 3 or 4 possibilities), much as standard [[linear regression]] is used for modeling [[continuous variable]]s (e.g., income or population). Specifically, logistic regression models can be phrased as [[latent variable]] models with [[error variable]]s following a logistic distribution. This phrasing is common in the theory of [[discrete choice]] models, where the logistic distribution plays the same role in logistic regression as the [[normal distribution]] does in [[probit regression]]. Indeed, the logistic and normal distributions have a quite similar shape. However, the logistic distribution has [[heavy-tailed distribution|heavier tails]], which often increases the [[robust statistics|robustness]] of analyses based on it compared with using the normal distribution.