Part 2. MM4XL Tools > 1. Strategic Tools > Risk Analyst > 4. Functions > 3. Distribution Functions > mmPOISSON(Mean)

Risk Analyst

mmPOISSON(Mean)

Example

=mmPOISSON(1) can equal 0.

Application

This is a very popular distribution used to model the number of events that will occur given a known mean occurrence value. It is useful to estimate the number of defects in a unit, incoming calls, insurance claims, customer arrivals, and much more.

How to use

Say we are modeling the purchase cycle of a washing powder market of 20 million potential buyers. The mmPOISSON distribution can be used to estimate the population at different points in time. When the parameter of the mmPOISSON is very large, however, the result is approximated and the difference between runs may become negligible.

=mmPOISSON(20000000)

An example where the parameter of the mmPOISSON distribution is not too large could be the number of appointments made every day by a sales representative. If the average number of daily visits for a rep is 6, the formula below helps to model this instance:

=mmPOISSON(6)

Technical profile

Type Discrete distribution.
Syntax =mmPOISSON(Mean)
Domain  Monte Carlo Simulation Software: Management Process Risk Analysis, an integer.
Mode If Mean = integer then = Mean <= x <= Mean-1
Otherwise = Mean
Parameters Mean = a > 0.
Remarks If Mean is nonnumeric mmPOISSON returns the #VALUE! error value.
Relationships It is related to the Binomial variate with parameter b = 1/c.
For large values it may be approximated by the Normal variate.
With parameters tending to infinity the Hypergeometric variate tends to a Poisson variate.
Graphs
mmPOISSON(1) mmPOISSON(10)
 Monte Carlo Simulation Software: Management Process Risk Analysis  Monte Carlo Simulation Software: Management Process Risk Analysis
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