The logistic distribution is a continuous probability distribution that looks similar to the normal distribution but has heavier tails and a slightly sharper peak. It is defined by two parameters: a location parameter (μ) and a scale parameter (s). The distribution is symmetric around its mean.
The logistic distribution is used in logistic regression and models growth processes, population spread, and probabilities of binary outcomes. Because of its heavier tails, it can sometimes model real-world data better than the normal distribution when more extreme values occur.
For example, in logistic regression, the logistic function models the probability that a person will click an online ad based on age and browsing behavior.
The graph below displays three logistic distributions with different location parameters (spread).
