1 / 21

Chapter 6. Discrete Probability Distributions

Chapter 6. Discrete Probability Distributions. What Is A Probability Distributions. Probability distributions gives the entire range of values that can occur based on an experiment. Probability distributions:

hiero
Télécharger la présentation

Chapter 6. Discrete Probability Distributions

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 6. Discrete Probability Distributions Ir. Muhril A., M.Sc., Ph.D.

  2. What Is A Probability Distributions • Probability distributions gives the entire range of values that can occur based on an experiment. • Probability distributions: A listing of all the outcomes of an experiment and the probability associated with each outcome. Ir. Muhril A., M.Sc., Ph.D.

  3. Example: Suppose we are interested in the number of heads showing face up on three tosses of a coin. This is the experiment. The possible results are: • zero heads, • One head, • Two heads, and • Three heads. What is the probability distribution for the number of heads? Ir. Muhril A., M.Sc., Ph.D.

  4. The Results Are: Ir. Muhril A., M.Sc., Ph.D.

  5. Probability Distribution For The Event Of Zero, One, Two, And Three Heads From Three Tosses Of Coin Ir. Muhril A., M.Sc., Ph.D.

  6. What Is A Probability Distributions (continued) • Character Of A Probability Distribution: 1. The probability of a particular outcome is between 0 and 1 inclusive. 2. The outcome are mutually exclusive. 3. The sum of the probabilities of the various events is equal to 1. Ir. Muhril A., M.Sc., Ph.D.

  7. Random Variables • Random Variable: a quantity resulting from an experiment that, by chance, can assume different values. • A random variable may be either discrete or continuous. Ir. Muhril A., M.Sc., Ph.D.

  8. Random Variables (continued) • Discrete Random Variable: - that can assume only certain clearly separated values. • Continuous Random Variable: - can assume one of an infinitely large number of values, within certain limitations. Ir. Muhril A., M.Sc., Ph.D.

  9. The Mean, Variance, And Standard Deviation Of A Discrete Probability Distributions • Mean: - represent the central location of a probability distributions. - the long run average value of the random variable. - referred to as its expected value. Equation 6-1 Page 185 (Lind). m = S{X.P(X)} Ir. Muhril A., M.Sc., Ph.D.

  10. The Mean, Variance, And Standard Deviation Of A Discrete Probability Distributions (continued) • Variance And Standard Deviation: Equation 6-2 Page 185 (Lind). s2 = S{(X – m)2P(X)} Ir. Muhril A., M.Sc., Ph.D.

  11. Example: John Ragsdale sells new cars for Pelican Ford. John usually sells the largest number of cars on Saturday. He has developed the following probability distribution for the number of cars he expects to sell on a particular Saturday Ir. Muhril A., M.Sc., Ph.D.

  12. Example: Ir. Muhril A., M.Sc., Ph.D.

  13. Example: • On a typical Saturday, how many cars does John expect to sell? m = S{X.P(X)} = 0(0.10) + 1(0.20) + 2(0.30) + 3(0.30) + 4(0.10) = 2.1 Ir. Muhril A., M.Sc., Ph.D.

  14. Binomial Probability Distribution • Widely occurring discrete probability distribution. • There are only two possible outcomes on a particular trial of an experiment. • The outcomes are mutually exclusive. Equation 6-3 Page 190 (Lind). P(X) = nCx px (1-p)n-x P(X) = binomial probability n = the number of trials C = combination x = the random variable defined as the number of successes p = the probability of a success on each trial Ir. Muhril A., M.Sc., Ph.D.

  15. Binomial Probability Distribution (continued) • Mean Of A Binomial Distribution: - equation 6-4 Page 191 (Lind). m = np • Variance Of A Binomial Distribution: - equation 6-5 Page 191 (Lind). s2 = np(1-p) Ir. Muhril A., M.Sc., Ph.D.

  16. Example: There are five flights daily from Pittsburg via US Airways into the Bradford, Pennsylvania, Regional Airport. Suppose the probability that any flight arrives late is 0.20. • What is the probability that none of the flight are late today? • What is the probability that exactly one of the flights is late today? Ir. Muhril A., M.Sc., Ph.D.

  17. Example: p = 0.20 n = 5 x = the number of successes case a success is a plane that arrives late. Ir. Muhril A., M.Sc., Ph.D.

  18. Example • No late arrivals (x = 0) P(0) = 5C0(0.20)0(1-0.20)5-0 = 0.3277 • The probability that exactly one of the five flights will arrive late today is: P(1) = 5C1(0.20)1(1-0.20)5-1 = 0.4096 Ir. Muhril A., M.Sc., Ph.D.

  19. Hypergeometric Probability Distribution • An outcome on each trial of an experiment is classified into one of two mutually exclusive categories, a success or a failure. • The random variable is the number of successes in a fixed number of trials. • The trials are not independent. • We assume that we sample from a finite population without replacement and n/N>0.05. Ir. Muhril A., M.Sc., Ph.D.

  20. Hypergeometric Distribution: P(x) = [(sCx) (N-sCn-x)] / (NCn) s = the number of successes in the population C = combination x = the number of successes in the sample N = the size of population n = the size of the sample or the number of trials Ir. Muhril A., M.Sc., Ph.D.

  21. Poisson Probability Distribution • Describes the number of times some event occurs during a specified interval. • The interval may be time, distance, area, or volume. Equation 6-7 Page 203 (Lind). Equation 6-8 Page 204 (Lind). Example Page 204. Ir. Muhril A., M.Sc., Ph.D.

More Related