Below you will find complete descriptions and links to 5 different analytics calculators for computing Poisson tests and Poisson values.

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Compute cumulative probabilities for a Poisson outcome, given the expected number of event occurrences per interval, the total number of events observed, and the total number of intervals. The calculator will compute P(K<k), P(K≤k), P(K>k), and P(K≥k) for the total number of observed event occurrences *k*. Knowing the cumulative probabilities for a Poisson outcome is often very useful in analytics studies that use Poisson experiments.

Compute the cumulative distribution function (CDF) for the Poisson distribution, given the expected number of event occurrences, and the observed number of event occurrences. The Poisson distribution CDF is very useful in analytics studies for assessing probabilities of events occurring within a specific interval.

Compute the probability mass function (PMF) for the Poisson distribution, given the expected number of event occurrences and the observed number of event occurrences. The Poisson distribution PMF identifies the likelihood that an associated discrete random variable will have an exact value, and is very useful for analytics studies that involve Poisson probabilities.

Compute the exact 90%, 95%, and 99% confidence intervals for a Poisson mean, given the total number of number of event occurrences. Knowing the confidence interval for a Poisson mean can be very useful for analytics studies that use the Poisson distribution to examine interval data.

Compute a Poisson probability, given the expected number of event occurrences per interval, the total number of events observed, and the total number of intervals. Knowing the probability of a specific number of events occurring within a specific number of fixed intervals is often very useful in analytics studies that use Poisson-distributed data.