These functions give you access to the Poisson distribution. Suppose you have independent events that occur, on the average, lambda times per second. The distribution of the number of events in a second is Poisson with parameter lambda. The distribution of intervals is exponential with parameter (1/ lambda). For more information about Fathom's distribution functions, click here.
poissonProbability(x, lambda, scale, min) poissonCumulative(x, lambda, scale, min) poissonQuantile(x, lambda, scale, min) lambda = intensity parameter
(also mean) |
The probability, cumulative, and quantile functions for the Poisson distribution. Only the independent variable x is required. The intensity parameter lambda must be positive. The other parameters scale and min provide dilation and translation, respectively. defaults: lambda = 1, min = 0, scale = 1. |
You may also draw random values from this distribution:
randomPoisson (mu, scale, min) |
A random value drawn from that distribution. |
This graph might help you see the effect of the lambda parameter. Increasing lambda moves the distribution to the right and makes it more spread out.
See also statistical functions (for mean( ) and its cousins).