The POISSON function calculates the Poisson distribution. This is useful for predicting the probability of a number of events over a specific measure, for example time, length or area.
The arguments of the function represent the following:
x |
the number of events, for which we seek the probability |
mean |
the known average rate of events |
cumulative |
a logical value, which determines the form of the function: TRUE calculates the cumulative Poisson probability, which gives the probability that the number of events will be between 0 and x inclusive. FALSE calculates the Poisson probability mass function, which gives the probability that the number of events will be exactly x. |
For example, if 7 cars per minute is the average traffic rate on a particular road, and their arrival is entirely random, we can calculate the probability of there being exactly 4 cars in a minute by using the formula:
POISSON(4, 7, FALSE)
which returns a probability of 0.091226.
The probability that there will be between 0 and 4 cars per minute inclusive is given by:
POISSON(4, 7, TRUE)
that is 0.172992.
See also: