Syntax
LOGNORM.DIST(x, mean, standard_dev, cumulative)
| Parameter | Description |
|---|---|
| x | Parameter of the LOGNORM.DIST function. |
| mean | Parameter of the LOGNORM.DIST function. |
| standard_dev | Parameter of the LOGNORM.DIST function. |
| cumulative | Parameter of the LOGNORM.DIST function. |
Examples
Probability stock stays below $150
Formula
=LOGNORM.DIST(150, LN(100), 0.3, TRUE)
Returns ~0.9129. There is about a 91.3% chance the stock price stays below $150 given the assumed parameters.
PDF at a specific point
Formula
=LOGNORM.DIST(4, 3.5, 1.2, FALSE)
Returns the probability density at x=4. This is not a probability itself but the height of the distribution curve at that point.
Probability of home price below $400K
Formula
=LOGNORM.DIST(400000, LN(300000), 0.4, TRUE)
Returns ~0.7422. About 74% of homes are expected to be priced below $400K under these lognormal assumptions.
Common Errors
#NUM!
x must be greater than 0 (lognormal is undefined for zero or negative values), and standard_dev must be positive.
#VALUE!
Occurs if any argument is non-numeric.
Tips
Mean parameter is of the log
The mean and standard_dev arguments refer to the parameters of the underlying normal distribution (i.e., ln(x)), not the raw data. Use LN() of your data's geometric mean.
Use TRUE for cumulative questions
Set cumulative to TRUE when you want 'probability of being at or below x'. Use FALSE only when you need the density function value.
Model right-skewed data
Lognormal is ideal for data that clusters near zero but has a long right tail, like income distributions, insurance claims, or time-to-failure.
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