Chapter 6 : Discrete Probability Distributions

Chapter 6 : Discrete Probability Distributions

1. A listing of all the outcomes of an experiment and the probability associated with each outcome.

2. Charateristics

   • The probability of a particular outcome is between 0 and 1 inclusive.
   • The outcomes are mutually exclusive events.
   • The list is exhaustive. So the sum of the probabilities of the various events is equal to 1.

3. Random Variables : A quantity resulting from an experiment that, by chance, can assume different values

   • DISCRETE RANDOM VARIABLE : A random variable that can assume only certain clearly separated values
   • CONTINUOUS RANDOM VARIABLE : assume an infinite number of values within a given range.

4. MEAN

   • typical value used to represent the central location of a probability distribution

5. Variance and Standard Deviation

   • Measures the amount of spread in a distribution

6. Binomial Probability Distribution

   • A Widely occurring discrete probability distribution
   • Charateristic
     • There are only two possible outcomes on a particular trial of an experiment
     • The outcomes are mutually exclusive
     • The random variable is the result of counts.
     • Each trial is independent of any other trial

7. Hypergeometric Probability Distribution

8. Poisson Probability Distribution

   • the number of times some event occurs during a specified interval. The interval may be time, distance, area, or volume.
   • P (x, μ) = (e−μ)(μx)/x!

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