Logistic distribution

Logistic distribution
Probability density function
Cumulative distribution function
Parameters	${\displaystyle \mu ,}$ location (real) ${\displaystyle s>0,}$ scale (real)
Support	${\displaystyle x\in (-\infty ,\infty )}$
PDF	${\displaystyle {\frac {e^{-(x-\mu )/s}}{s\left(1+e^{-(x-\mu )/s}\right)^{2}}}}$
CDF	${\displaystyle {\frac {1}{1+e^{-(x-\mu )/s}}}={\frac {1+\tanh {\frac {x-\mu }{2s}}}{2}}}$
Quantile	${\displaystyle \mu +s\log \left({\frac {p}{1-p}}\right)}$
Mean	${\displaystyle \mu }$
Median	${\displaystyle \mu }$
Mode	${\displaystyle \mu }$
Variance	${\displaystyle {\frac {s^{2}\pi ^{2}}{3}}}$
Skewness	${\displaystyle 0}$
Excess kurtosis	${\displaystyle 6/5}$
Entropy	${\displaystyle \ln s+2}$
MGF	${\displaystyle e^{\mu t}\mathrm {B} (1-st,1+st)}$ for ${\displaystyle t\in (-1/s,1/s)}$ and ${\displaystyle \mathrm {B} }$ is the Beta function
CF	${\displaystyle e^{it\mu }{\frac {\pi st}{\sinh(\pi st)}}}$

In probability theory and statistics, the logistic distribution is a continuous probability distribution. Its cumulative distribution function is the logistic function, which appears in logistic regression and feedforward neural networks. It resembles the normal distribution in shape but has heavier tails (higher kurtosis). The logistic distribution is a special case of the Tukey lambda distribution.