Chapter Contents

• Counting and Probability - Introduction
• 1. Factorial Notation
• 2. Basic Principles of Counting
• 3. Permutations
• 4. Combinations
• 5. Introduction to Probability Theory
• 6. Probability of an Event
• Singapore TOTO
• Probability and Poker
• 7. Conditional Probability
• 8. Independent and Dependent Events
• 9. Mutually Exclusive Events
• 10. Bayes’ Theorem
• 11. Probability Distributions - Concepts
• 12. Binomial Probability Distributions
• 13. Poisson Probability Distribution
• 14. Normal Probability Distribution
• The z-Table
• Counting and Probability Problem Solver

• Comments, Questions?

Feelings about Math

Recommendation

Easy to understand math lessons on DVD. Try before you commit. More info:
MathTutorDVD.com

Online Algebra Solver

Solve your algebra problem step by step!

Online Algebra Solver »

Share

Share this page.

From the math blog...

13. The Poisson Probability Distribution

The Poisson Distribution was developed by the French mathematician Simeon Denis Poisson in 1837.

The Poisson random variable satisfies the following conditions:

1. The number of successes in two disjoint time intervals is independent.

2. The probability of a success during a small time interval is proportional to the entire length of the time interval.

Apart from disjoint time intervals, the Poisson random variable also applies to disjoint regions of space.

Applications

• the number of deaths by horse kicking in the Prussian army (first application)

• birth defects and genetic mutations

• rare diseases (like Leukemia, but not AIDS because it is infectious and so not independent) - especially in legal cases

• car accidents

• traffic flow and ideal gap distance

• number of typing errors on a page

• hairs found in McDonald's hamburgers

• spread of an endangered animal in Africa

• failure of a machine in one month

The probability distribution of a Poisson random variable X representing the number of successes occurring in a given time interval or a specified region of space is given by the formula:

where

x = 0, 1, 2, 3...

e = 2.71828 (but use your calculator's e button)

μ = mean number of successes in the given time interval or region of space

Mean and Variance of Poisson Distribution

If μ is the average number of successes occurring in a given time interval or region in the Poisson distribution, then the mean and the variance of the Poisson distribution are both equal to μ.

E(X) = μ

and

V(X) = σ² = μ

Note: In a Poisson distribution, only one parameter, μ is needed to determine the probability of an event.

EXAMPLE 1

A life insurance salesman sells on the average 3 life insurance policies per week. Use Poisson's law to calculate the probability that in a given week he will sell

1. Some policies

2. 2 or more policies but less than 5 policies.

3. Assuming that there are 5 working days per week, what is the probability that in a given day he will sell one policy?

Answer

EXAMPLE 2

Twenty sheets of aluminum alloy were examined for surface flaws. The frequency of the number of sheets with a given number of flaws per sheet was as follows:

Number of flaws	Frequency
0	4
1	3
2	5
3	2
4	4
5	1
6	1

What is the probability of finding a sheet chosen at random which contains 3 or more surface flaws?

Answer

EXAMPLE 3

If electricity power failures occur according to a Poisson distribution with an average of 3 failures every twenty weeks, calculate the probability that there will not be more than one failure during a particular week.

Answer

EXAMPLE 4

Vehicles pass through a junction on a busy road at an average rate of 300 per hour.

1. Find the probability that none passes in a given minute.

2. What is the expected number passing in two minutes?

3. Find the probability that this expected number actually pass through in a given two-minute period.

Answer

EXAMPLE 5

A company makes electric motors. The probability an electric motor is defective is 0.01. What is the probability that a sample of 300 electric motors will contain exactly 5 defective motors?

Answer