1. Poisson Distribution The Poisson Distribution is used for Discrete events (those you can count) In a continuous but finite interval of time and space The events can be counted and occur randomly at any time or place. The is no upper limit of events. λ (lambda) is the measurement we will use in the formula and is the mean number of occurrences Examples: X= the number of earthquakes in NZ over 6.0 on the Richter scale per year. λ = 4 X= the number of defects in a 5km telecommunications cable. λ = 3.65

2. Poisson Distribution

7. Using the tables: X is a random variable with mean 2. Find a.P(X ≥ 3) P = 0.3232

8. Poisson Distribution Using the tables: X is a random variable with mean 2. Find a.P(X ≤ 1) P = 0.406

9. Poisson Distribution The number of mature toheroa per square metre at a West Coast beach has a Poisson distribution with a mean of 1.6. When an area of 3 m 2 is searched, what is the probability that fewer than 6 will be found? λ = 3 x 1.6 = 4.8 x < 6 ie 0, 1, 2, …, 5 0.0082 0.0395 0.0948 0.1517 0.1820 0.1747 0.6509