Chapter 12 Continuous Random Variables and their Probability Distributions - PowerPoint PPT Presentation

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Chapter 12 Continuous Random Variables and their Probability Distributions

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... Uniform Distribution Normal Distribution Exponential Distribution Erlang and Gamma Distributions Weibull Distribution Lognormal ... PPT Template Equation Chapter ... – PowerPoint PPT presentation

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Title: Chapter 12 Continuous Random Variables and their Probability Distributions


1
Chapter 12Continuous Random Variables and
their Probability Distributions
2
Probability Distributions of a Continuous Random
Variable
  • For a continuous random variable X, a probability
    density function such that

3
Probability Distributions of a Continuous Random
Variable
  • For a continuous random variable X, a probability
    cumulative function

4
Mean Standard Deviation of a Continuous Random
Variable
5
Continuous Probability Distributions
  • Continuous Uniform Distribution
  • Normal Distribution
  • Exponential Distribution
  • Erlang and Gamma Distributions
  • Weibull Distribution
  • Lognormal Distribution
  • Beta Distribution

6
Continuous Uniform Distribution
  • Probability Density Function
  • Mean
  • Variance

7
Normal Distribution
  • Probability Density Function, with parameter ?,
    where -?lt?lt?, and ?gt0
  • Mean
  • Variance

8
Normal Distribution
  • The curve is symmetric about the mean
  • The mean, median, and mode are equal
  • The tails of the curve extend indefinitely

9
Standard Normal Distribution
  • A normal random variable with parameter ?0, and
    ?1
  • Cumulative Distribution
  • Table II in Appendix A
  • Convert x to z

10
Standard Normal Distribution
www.barringer1.com/jan98f1.gif
11
Exponential Distribution
  • Probability Density Function, with mean ?, where
    ?gt0, and xgt0
  • Cumulative probability
  • Mean
  • Variance

12
Exponential Distribution
13
Exponential DistributionAlternate Definition
  • Probability Density Function, with rate ?, where
    ?gt0, and x?0
  • Cumulative probability
  • Mean
  • Variance

14
Exponential DistributionExample
  • An HR department wishes to study the need for
    hiring new secretaries. It is estimated that the
    amount of time that a secretary stays in the job
    can be described as an exponential distribution
    with a mean of 26 months. The company just hired
    a new secretary. Calculate the probabilities of
    the following events
  • The secretary has to be replaced within the first
    year.
  • The secretary has to be replaced during the third
    year.
  • The secretary remains in the position for more
    than 5 years
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