# POISSON

Formulas / POISSON
Calculate the Poisson distribution.
`POISSON(x,mean,cumulative)`
• x - The number of events (non-negative integer) for which you want to calculate the Poisson probability.
• mean - The expected number of events (mean) that occur in a fixed interval. The mean must be a positive value.
• cumulative - A logical value that determines the type of distribution to calculate. If TRUE, the function calculates the cumulative distribution function (CDF); if FALSE, the function calculates the probability mass function (PMF).

## Examples

• `=POISSON(3,2.5,FALSE)`

For example, if you wanted to calculate the probability of three successes when the mean number of successes is 2.5, you would use the POISSON function as above. This would return 0.19358.

• `=POISSON(2,2,TRUE)`

If you wanted to calculate the cumulative Poisson probability for two successes when the mean number of successes is 2, you would use the POISSON function, such as above. This would return 0.67667.

• `=POISSON(5,4,FALSE)`

For example, if you wanted to calculate the probability of five successes when the mean number of successes is 4, you would use the POISSON function, such as above. This would return 0.17603.

• `=POISSON(3,5,TRUE)`

If you wanted to calculate the cumulative Poisson probability for three successes when the mean number of successes is 5, you would use the POISSON function such as above. This would return 0.72212.

## Summary

The POISSON function has been replaced by new functions with better accuracy that better reflect their usage. These new functions can be used to predict the number of events over a specific time period.

• The POISSON function has been replaced with new functions that better meet the needs of the data. These new functions have better accuracy than the POISSON function.