Lognormal Distribution Calculator
Compute lognormal distribution probabilities, quantiles, mean, variance, median, and mode from mu and sigma parameters.
What Is the Lognormal Distribution?
A lognormal distribution describes a continuous random variable $X$ where $\ln(X)$ follows a normal distribution. It applies to positive-valued data such as stock prices, income, and failure times. Compare with the Normal Distribution Calculator for the underlying normal model.
Key Formulas
The probability density function is:
$$f(x) = \frac{1}{x\sigma\sqrt{2\pi}} \exp\left(-\frac{(\ln x - \mu)^2}{2\sigma^2}\right)$$
Common measures:
$$\text{Mean} = e^{\mu + \sigma^2/2}, \quad \text{Median} = e^{\mu}, \quad \text{Mode} = e^{\mu - \sigma^2}$$
Example: Stock Price Threshold
With $\mu = 0$ and $\sigma = 1$, the probability that $X \leq 1$ is $P(X \leq 1) = \Phi(0) = 50\%$, where $\Phi$ is the standard normal CDF applied to $(\ln x - \mu)/\sigma$.
Frequently Asked Questions
What do mu and sigma represent in a lognormal distribution?
Mu and sigma are the mean and standard deviation of the natural logarithm of X, not of X itself. They parameterize the underlying normal distribution of ln(X).
Why can lognormal variables only be positive?
Since X = exp(Y) where Y is normally distributed, X is always positive. The exponential function never produces negative or zero values.
How is the lognormal CDF calculated?
The CDF at x is Phi((ln x - mu) / sigma), where Phi is the standard normal cumulative distribution function.
When should I use lognormal instead of normal?
Use lognormal when data is strictly positive and right-skewed, such as asset prices, income distributions, or particle sizes. Use normal for symmetric, unbounded data.
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