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  1. Introduction
  2. Graphing Distributions
  3. Summarizing Distributions
  4. Describing Bivariate Data

  5. Probability
    1. Contents
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    2. Introduction to Probability
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         Video
    3. Basic Concepts
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         Video
    4. Conditional p Demo
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    5. Gambler's Fallacy
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         Video
    6. Permutations and Combinations
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         Video
    7. Birthday Demo
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    8. Binomial Distribution
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         Video
    9. Binomial Demonstration
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    10. Poisson Distribution
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    11. Multinomial Distribution
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         Video
    12. Hypergeometric Distribution
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         Video
    13. Base Rates
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         Video
    14. Bayes Demo
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    15. Monty Hall Problem
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    16. Statistical Literacy
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    17. Exercises
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  6. Research Design
  7. Normal Distribution
  8. Advanced Graphs
  9. Sampling Distributions
  10. Estimation
  11. Logic of Hypothesis Testing
  12. Tests of Means
  13. Power
  14. Regression
  15. Analysis of Variance
  16. Transformations
  17. Chi Square
  18. Distribution Free Tests
  19. Effect Size
  20. Case Studies
  21. Calculators
  22. Glossary
 

Poisson Distribution

Author(s)

David M. Lane

Prerequisites

Logarithms

The Poisson distribution can be used to calculate the probabilities of various numbers of "successes" based on the mean number of successes. In order to apply the Poisson distribution, the various events must be independent. Keep in mind that the term "success" does not really mean success in the traditional positive sense. It just means that the outcome in question occurs.

Suppose you knew that the mean number of calls to a fire station on a weekday is 8. What is the probability that on a given weekday there would be 11 calls? This problem can be solved using the following formula based on the Poisson distribution:

poisson formula where

e is the base of natural logarithms (2.7183)
μ is the mean number of "successes"
x is the number of "successes" in question

For this example,

poisson formula

since the mean is 8 and the question pertains to 11 fires.

The mean of the Poisson distribution is μ. The variance is also equal to μ. Thus, for this example, both the mean and the variance are equal to 8.


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