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Introduction

Suppose an event can occur several times within a given unit of time. When the total number of occurrences of the event is unknown, we can think of it as a random variable. This random variable follows the Poisson Distribution. The Poisson distribution is a limiting case of the Binomial distribution when the number of trials becomes very large and the probability of success is small.
As we know from the previous article the probability of ‘x’ success in ‘n’ trials in a Binomial Experiment with success probability ‘p’, is
P(x) = (n / x)px(1 - p)n - x
Let us denote the Expected value of the Random Variable by λ. So-
Probability Distributions (Poisson Distribution) | Engineering Mathematics - Civil Engineering (CE)

Expected Value

The Expected Value of the Poisson distribution can be found by summing up products of Values with their respective probabilities.
Probability Distributions (Poisson Distribution) | Engineering Mathematics - Civil Engineering (CE)

Variance and Standard Deviation

The Variance of the Poisson distribution can be found using the Variance Formula:
Var[X] = E[X2] - E[X]2 
Probability Distributions (Poisson Distribution) | Engineering Mathematics - Civil Engineering (CE)
Therefore we have Variance as:
Var[X] = E[X2] - E[X]2 
= λ2 + λ - λ2
= λ
Also the standard Deviation:
σ = √λ

Relation with Exponential Distribution

The number of occurrences of an event within a unit of time has a Poisson distribution with parameter λ if the time elapsed between two successive occurrences of the event has an exponential distribution with parameter λ and it is independent of previous occurrences.

Example: For the case of the thin copper wire, suppose that the number of flaws follows a Poisson distribution with a mean of 2.3 flaws per millimeter. Determine the probability of exactly two flaws in 2 millimeter of wire.
Solution: Let X denote the number of flaws in 1 millimeter of wire. The first step we need to do is to find the parameter λ which is nothing but the Expected value of the random variable. In this case we are given the Expected number of flaws in 1 millimeter of wire. We need to find the Expected number of flaws in 2 millimeter of wire.
Expected number of flaws in 2 millimeter of wire = 2 * np = 2λ = 2 * 2.3 = 4.6
Therefore,
Probability Distributions (Poisson Distribution) | Engineering Mathematics - Civil Engineering (CE)

The document Probability Distributions (Poisson Distribution) | Engineering Mathematics - Civil Engineering (CE) is a part of the Civil Engineering (CE) Course Engineering Mathematics.
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FAQs on Probability Distributions (Poisson Distribution) - Engineering Mathematics - Civil Engineering (CE)

1. What is a Poisson distribution?
Ans. A Poisson distribution is a probability distribution that represents the number of events that occur within a fixed interval of time or space. It is often used to model rare events or events that occur randomly and independently.
2. How is the Poisson distribution different from other probability distributions?
Ans. The Poisson distribution is different from other probability distributions, such as the normal distribution or binomial distribution, because it is specifically used for counting events that occur over a fixed interval of time or space. It assumes that the events occur randomly and independently, and the probability of an event occurring is constant within the interval.
3. What are the characteristics of a Poisson distribution?
Ans. The characteristics of a Poisson distribution include: - The mean and variance of the distribution are equal. - The shape of the distribution is skewed to the right. - The distribution is defined for non-negative integer values. - The probability of an event occurring is proportional to the length of the interval.
4. In what situations can the Poisson distribution be applied?
Ans. The Poisson distribution can be applied in various situations, including: - Modeling the number of phone calls received at a call center in a given hour. - Analyzing the number of accidents that occur on a particular stretch of road in a day. - Estimating the number of emails received per day. - Predicting the number of defects in a manufacturing process.
5. How is the Poisson distribution used in practice?
Ans. The Poisson distribution is used in practice by calculating probabilities of specific events occurring within a fixed interval. It helps in making predictions and estimating the likelihood of rare events. Additionally, it is used in statistical hypothesis testing and quality control to assess if the observed number of events is significantly different from the expected number based on the Poisson distribution.
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