Poisson Distribution Probability Calculator

The Poisson distribution is a popular discrete probability distribution in statistics that expresses the probability of a given number of events occurring in a fixed interval of time or space. This calculator computes Poisson probabilities for a given mean rate (λ) and random variable (𝑥), and visualizes the distribution.

Average rate of success (𝛌):

Random variable (𝑥):

P(X = ):

P(X < ):

P(X ≤ ):

P(X > ):

P(X ≥ ):

Understanding the Poisson Distribution

The Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space, provided that the events happen independently of each other and occur with a constant average rate (λ). It is commonly applied in areas such as telecommunications (e.g., the number of calls received by a call center), biology (e.g., the number of mutations in a DNA sequence), and traffic engineering (e.g., the number of vehicles passing through a toll booth).

Key Components of the Poisson Distribution

Average Rate (λ): This is the average number of occurrences of an event within a given time frame or space. For example, if a website receives an average of 3 user signups per hour, then λ = 3.
Random Variable (X): The random variable X represents the number of occurrences of the event you are measuring. For example, X could be the number of user signups in a particular hour.

Poisson Distribution Formula

The probability mass function (PMF) for the Poisson distribution is given by the formula:

\[ P(X = x) = \frac{e^{-\lambda} \lambda^x}{x!}, \quad \text{where} \ x = 0, 1, 2, \dots \]

Here, \( e \) is the base of the natural logarithm (approximately equal to 2.718), and \( x! \) represents the factorial of \( x \). The Poisson formula calculates the probability of observing exactly \( x \) events given the average rate \( \lambda \).

Conditions for Using the Poisson Distribution

The Poisson distribution applies under specific conditions:

Event Independence: The events must occur independently, meaning that the occurrence of one event does not affect the probability of another event occurring.
Constant Average Rate (λ): The average rate \( λ \) must remain constant over time or space.
Discreteness: The number of events (X) must be a non-negative integer. In other words, X = 0, 1, 2, 3, etc.

Step-by-Step Example: Finding Poisson Probability

Suppose we want to calculate the probability of receiving exactly 4 customer inquiries in an hour, given that the average rate of customer inquiries is 3 per hour.

Step 1: Identify the Key Parameters

In this case:

\( \lambda = 3 \): The average rate of customer inquiries per hour.
\( X = 4 \): The number of customer inquiries we are interested in.

Step 2: Apply the Poisson Formula

Using the Poisson formula:

\[ P(X = 4) = \frac{e^{-3} 3^4}{4!} \]

First, calculate \( 4! \) (which equals 24) and \( e^{-3} \approx 0.0498 \). Then, substitute these values into the formula:

\[ P(X = 4) = \frac{0.0498 \times 81}{24} = 0.168 \]

Therefore, the probability of receiving exactly 4 customer inquiries in an hour is approximately 0.168, or 16.8%.

Other Useful Probability Calculations

The Poisson distribution can also be used to calculate cumulative probabilities, such as the probability of observing fewer than or greater than a specific number of events. These can be useful in different scenarios:

Less than \( x \) (P(X < \( x \))): The cumulative probability that the number of events is less than a given value.
Greater than \( x \) (P(X > \( x \))): The cumulative probability that the number of events is greater than a given value.
Less than or equal to \( x \) (P(X ≤ \( x \))): The cumulative probability that the number of events is less than or equal to a given value.
Greater than or equal to \( x \) (P(X ≥ \( x \))): The cumulative probability that the number of events is greater than or equal to a given value.

Practical Applications of the Poisson Distribution

The Poisson distribution is widely used in real-world applications, such as:

Call Centers: Estimating the number of incoming calls in a given time period.
Traffic Flow: Predicting the number of vehicles passing through a toll booth or intersection during peak hours.
Biology: Modeling the number of mutations occurring in a DNA sequence over a given time or space.
Finance: Assessing the frequency of rare events, such as defaults in a portfolio of loans.