Log-normal distribution

In probability and statistics, the log-normal distribution is a probability distribution which is closely related to the normal distribution: if X is a random variable with a normal distribution, then exp(X) has a log-normal distribution. In other words: the natural logarithm of a log-normally distributed variable is normally distributed.

"Log-normal" is also written "log normal" or "lognormal".

A variable might be modeled as log-normal if it can be thought of as the multiplicative product of many small independent factors. A typical example is the long-term return rate on a stock investment: it can be considered as the product of the daily return rates.

The log-normal distribution has probability density function

<math>f(x) = \frac{1}{x \sigma \sqrt{2 \pi}} e^{-(\ln x - \mu)^2/2\sigma^2}<math>

for x > 0, where μ and σ are the mean and standard deviation of the variable's logarithm. The expected value is

<math>\mathrm{E}(X) = e^{\mu + \sigma^2/2}<math>

and the variance is

<math>\mathrm{var}(X) = (e^{\sigma^2} - 1) e^{2\mu + \sigma^2}<math>.

Relationship to geometric mean and geometric standard deviation

The log-normal distribution, the geometric mean, and the geometric standard deviation are related. In this case, the geometric mean is equal to <math>\exp(\mu)<math> and the geometric standard deviation is equal to <math>\exp(\sigma)<math>.

If a sample of data is determined to come from a log-normally distributed population, the geometric mean and the geometric standard deviation may be used to estimate confidence intervals akin to the way the arithmetic mean and standard deviation are used to estimate confidence intervals for a normally distributed sample of data.

Confidence interval bounds	log space	geometric
3σ lower bound	<math>\mu - 3\sigma<math>	<math>\mu_{geo} / \sigma_{geo}^3<math>
2σ lower bound	<math>\mu - 2\sigma<math>	<math>\mu_{geo} / \sigma_{geo}^2<math>
1σ lower bound	<math>\mu - \sigma<math>	<math>\mu_{geo} / \sigma_{geo}<math>
1σ upper bound	<math>\mu + \sigma<math>	<math>\mu_{geo} \sigma_{geo}<math>
2σ upper bound	<math>\mu + 2\sigma<math>	<math>\mu_{geo} \sigma_{geo}^2<math>
3σ upper bound	<math>\mu + 3\sigma<math>	<math>\mu_{geo} \sigma_{geo}^3<math>

Where geometric mean <math>\mu_{geo} = \exp(\mu)<math> and geometric standard deviation <math>\sigma_{geo} = \exp(\sigma)<math>

See also

geometric mean, geometric standard deviation