The LOGNORM.DIST function in Google Sheets is crucial for evaluating the log-normal distribution of a given variable. This statistical function is useful for modeling data that is positively skewed, such as income or population growth, where values cannot be negative but can have a wide range.

Syntax

LOGNORM.DIST(x, mean, standard_dev, cumulative)

• x: The value for which you want the distribution.
• mean: The mean of the natural logarithm of the distribution.
• standard_dev: The standard deviation of the natural logarithm of the distribution.
• cumulative: A boolean value indicating the type of distribution—if TRUE, it returns the cumulative distribution function; if FALSE, it returns the probability density function.

Example #1

LOGNORM.DIST(20, 15, 5, TRUE)

This function calculates the cumulative distribution function for a value of 20, with a mean of 15 and a standard deviation of 5. The result may look like 0.847.

Example #2

LOGNORM.DIST(10, 5, 2, FALSE)

Here, the function returns the probability density for a value of 10, mean of 5, and standard deviation of 2, yielding a result such as 0.160.

Example #3

LOGNORM.DIST(30, 20, 10, TRUE)

This evaluates the cumulative log-normal distribution for 30, with parameters yielding a result like 0.844, indicating how likely it is to observe a value less than or equal to 30.

Error handling

• NUM!: Occurs when the standard deviation (standard_dev) is non-positive or if ‘x’ is negative. Ensure that the standard deviation is greater than zero and that ‘x’ is not negative.
• VALUE!: Happens when the arguments are of incorrect types. Make sure that ‘mean’ and ‘standard_dev’ are numbers.
• NAME?: Indicates that the function name is not recognized, possibly due to a spelling mistake. Check the spelling of LOGNORM.DIST.

Conclusion

In summary, the LOGNORM.DIST function is a powerful tool for analyzing data that follows a log-normal distribution. By understanding its parameters, users can effectively model real-world phenomena, making informed decisions based on statistical analysis.