Statistics - Poisson Distribution

 
   
     
       

Poisson Distribution

       

Understanding the Poisson Distribution

       

          The Poisson Distribution models the probability of a given number of events occurring in a fixed interval of time or space, provided the events happen independently and at a constant average rate. It is a discrete probability distribution.        

        Poisson Distribution Graph      
     
       
         

Key Notations:

         
               
  • \( \lambda \): average number of occurrences in a fixed interval
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  • \( x \): number of occurrences
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  • \( e \): Euler’s number (~2.71828)
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Formulas:

         

Mean and Variance:

         

\[ \mu = \lambda, \quad \sigma^2 = \lambda \]

         

Probability Mass Function (PMF):

         

            \[             f(x) = \frac{\lambda^x e^{-\lambda}}{x!}             \]          

         

Cumulative Distribution Function (CDF):

         

            \[             F(x_j) = \sum_{k \leq j} \frac{\lambda^k e^{-\lambda}}{k!}             \]          

         

Key Properties of Poisson Distribution:

         
               
  • Discrete distribution for non-negative integers (0, 1, 2, ...)
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  • Events occur independently
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  • Mean equals variance: \( \mu = \sigma^2 = \lambda \)
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  • Used when \( n \) is large and \( p \) is small, \( \lambda = np \)
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Applications of Poisson Distribution:

         
               
  • Number of customer arrivals at a store per hour
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  • Number of emails received in a minute
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  • Defects in a manufacturing process per unit
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  • Call center incoming call frequency
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  • Modeling rare natural events (e.g., earthquakes)
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