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📊 Poisson Distribution Calculator

Calculate P(X=k), P(X≤k), and P(X>k) for a Poisson distribution. Visualise the probability mass function as a bar chart.

Poisson Distribution Formulas

PMF
P(X = k) = (λᵏ × e⁻λ) / k!
Properties
Mean = λ
Variance = λ
Std Dev = √λ

Common Applications of Poisson Distribution

  1. 1
    Call Centre Modelling
    Estimating the probability of receiving exactly k calls per hour when the average rate is λ calls/hour.
  2. 2
    Quality Control
    Modelling the number of defects per unit of product when defects occur rarely and independently.
  3. 3
    Medical Events
    Predicting the number of hospital admissions per day for rare conditions.
  4. 4
    Traffic Analysis
    Modelling the number of vehicles passing a checkpoint per minute.

Frequently Asked Questions

The Poisson distribution models the number of events occurring in a fixed interval of time or space, given that events occur independently at a constant average rate λ. It is used for rare events.

Lambda is the average number of events per interval. It is both the mean and variance of the Poisson distribution. A larger λ shifts the distribution to the right and makes it more symmetric.

Use Poisson when n is large and p is small (rare events), such that np ≈ λ. The Poisson distribution approximates the binomial when n > 20 and p < 0.05.

The cumulative probability P(X ≤ k) is the sum of P(X = 0) + P(X = 1) + ... + P(X = k). It tells you the probability of observing at most k events.

Yes. The Poisson distribution is a discrete probability distribution — k must be a non-negative integer (0, 1, 2, ...). There is no upper limit on k, though probabilities become negligible after a few multiples of λ.

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