# LOGNORM.INV

The LOGNORM.INV function returns the inverse of the standard normal cumulative distribution for a specified mean and standard deviation.

## Syntax

=LOGNORM.INV(`probability`, `mean`, `standard_dev`)

When you're dealing with statistical analysis and need to determine the inverse of the standard normal cumulative distribution, the LOGNORM.INV function in Excel comes to the rescue. This function is particularly helpful in scenarios where you have data with a known mean and standard deviation and you want to find the input value that corresponds to a given probability under a lognormal distribution model. It's a handy tool for probability calculations in various fields such as finance, engineering, and more.

## Examples

Suppose you have a dataset with a mean of 10 and a standard deviation of 2. You want to find the inverse of the standard normal cumulative distribution for a probability of 0.75. The formula would be: =LOGNORM.INV(0.75, 10, 2)

Imagine you are conducting a financial analysis and you know that the mean return on an investment is 8% with a standard deviation of 3%. You wish to determine the point where the probability of achieving returns higher than 15% is 0.10. The LOGNORM.INV formula to use is: =LOGNORM.INV(0.10, 0.08, 0.03)

## Questions

How does the LOGNORM.INV function work?

The LOGNORM.INV function calculates the inverse of the standard normal cumulative distribution for a specific mean and standard deviation. It allows you to find the input value that corresponds to a given probability under a lognormal distribution model.

What is the significance of the mean and standard deviation in the LOGNORM.INV function?

The mean and standard deviation specified in the LOGNORM.INV function represent the average and variability of the dataset for which you are determining the inverse standard normal cumulative distribution. These parameters help define the shape and characteristics of the lognormal distribution.

Can the LOGNORM.INV function be used for non-lognormal distributions?

No, the LOGNORM.INV function is specifically designed for lognormal distributions. It may not provide accurate results if applied to datasets that do not follow a lognormal distribution pattern.

NORM.INV
NORM.S.INV
NORM.DIST
NORM.S.DIST