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The Poisson Distribution is a discrete distribution. It is named after Simeon-Denis Poisson (1781-1840), a French mathematician, who published its essentials in a paper in 1837. The Poisson distribution and the binomial distribution have some similarities, but also several differences.
The binomial distribution describes a distribution of two possible outcomes designated as successes and failures from a given number of trials. The Poisson distribution focuses only on the number of discrete occurrences over some interval. A Poisson experiment does not have a given have a given number of trials (n) as binomial experiment does. For example, whereas a binomial experiment might be used to determine how many black cars are in a random sample of 50 cars, a Poisson experiment might focus on the number of cars randomly arriving at a car wash during a 20-minute interval.
The Poisson distribution has the following characteristics:
The Poisson distribution is characterized by lambda , the mean number of occurrences in the interval. If a Poisson-distributed phenomenon is studied over a long period of time, is the long-run average of the process. The Poisson formula is used to compute the probability of occurrences over an interval for a given lambda value.
The Poisson Formula is
x = 0, 1, 2, 3, …
= mean number of occurrences in the interval
e = 2.718282
The following two videos show how to derive the Poisson Formula from the Binomial Formula.
Introduction to Poisson Processes and the Poisson Distribution.
More of the derivation of the Poisson Distribution.
The following video will discuss a situation that can be modeled by a Poisson Distribution, give the formula, and do a simple example illustrating the Poisson Distribution.
Suppose a fast food restaurant can expect two customers every 3 minutes, on average. What is the probability that four or fewer patrons will enter the restaurant in a 9 minute period?
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