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Unformatted text preview: Chapter 4 The Poisson Distribution 4.1 The Fish Distribution? The Poisson distribution is named after SimeonDenis Poisson (1781–1840). In addition, poisson is French for fish. In this chapter we will study a family of probability distributions for a countably infinite sample space, each member of which is called a Poisson Distribution . Recall that a binomial distribution is characterized by the values of two parameters: n and p . A Poisson distribution is simpler in that it has only one parameter, which we denote by θ , pronounced theta . (Many books and websites use λ , pronounced lambda, instead of θ .) The parameter θ must be positive: θ > . Below is the formula for computing probabilities for the Poisson. P ( X = x ) = e θ θ x x ! , for x = 0 , 1 , 2 , 3 , . . .. (4.1) In this equation, e is the famous number from calculus, e = lim n →∞ (1 + 1 /n ) n = 2 . 71828 . . .. You might recall from the study of infinite series in calculus, that ∞ summationdisplay x =0 b x /x ! = e b , for any real number b . Thus, ∞ summationdisplay x =0 P ( X = x ) = e θ ∞ summationdisplay x =0 θ x /x ! = 1 . Thus, we see that Formula 4.1 is a mathematically valid way to assign probabilities to the nonneg ative integers. The mean of the Poisson is its parameter θ ; i.e. μ = θ . This can be proven using calculus and a similar argument shows that the variance of a Poisson is also equal to θ ; i.e. σ 2 = θ and σ = √ θ . 33 When I write X ∼ Poisson( θ ) I mean that X is a random variable with its probability distribu tion given by the Poisson with parameter value θ . I ask you for patience. I am going to delay my explanation of why the Poisson distribution is important in science. Poisson probabilities can be computed by hand with a scientific calculator. Alternative, you can go to the following website: http://stattrek.com/Tables/Poisson.aspx I will give an example to illustrate the use of this site. Let X ∼ Poisson( θ ). The website calculates two probabilities for you: P ( X = x ) and P ( X ≤ x ) . You must give as input your value of θ and your desired value of x . Suppose that I have X ∼ Poisson(10) and I am interested in P ( X = 8) . I go to the site and type ‘8’ in the box labeled ‘Poisson random variable,’ and I type ‘10’ in the box labeled ‘Average rate of success.’ I click on the ‘Calculate’ box and the site gives me the following answers: P ( X = 8) = 0 . 1126 (Appearing as ‘Poisson probability’) and P ( X ≤ 8) = 0 . 3328 (Appearing as ‘Cumulative Poisson probability’). From this last equation and the complement rule, I get P ( X ≥ 9) = P ( X > 8) = 1 P ( X ≤ 8) = 1 . 3328 = 0 . 6672 ....
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 Fall '09
 ProfessorWardrop
 Statistics, Normal Distribution, Poisson Distribution, Probability, Probability theory, Binomial distribution, Poisson

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