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Chapter 3 discrete_distribution_rev_2009

Chapter 3 discrete_distribution_rev_2009






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    Chapter 3 discrete_distribution_rev_2009 Chapter 3 discrete_distribution_rev_2009 Presentation Transcript

    • CHAPTER 3 DISCRETE PROBABILITY DISTRIBUTION Discrete Probability Distribution
    • Jan 2009 : At the end of the lecture, you will be able to : - select an appropriate discrete probability distribution * binomial distribution or * poisson distribution to calculate probabilities in specific application - calculate the probability, means and variance for each of the discrete distributions presented DISCRETE PROBABILITY DISTRIBUTION Learning Objectives:
    • Jan 2009 Bernoulli Trials : experiment with two possible outcomes, either ‘ Success’ or ‘failure’. Probability of success is given as p and probability of failure is 1- p BINOMIAL DISTRIBUTION Bin(n,p) Requirements of a binomial experiment: * n Bernoulli trials * trials are independent * that each trial have a constant probability p of success. Example binomial experiment: tossing the same coin successively and independently n times
    • Jan 2009 BINOMIAL DISTRIBUTION Bin( n , p ) A binomial random variable X associated with a binomial experiment consisting of n trials is defined as: X = the number of ‘success’ among n trials The probability mass function of X is Mean and Variance: A random variable that has a binomial distribution with parameters n and p , is denoted by X ~ Bin ( n , p )
    • Jan 2009 Poisson Probability Distribution Conditions to apply the Poisson Probability distribution are: 1. x is a discrete random variable 2. The occurrences are random 3. The occurrences are independent Useful to model the number of times that a certain event occurs per unit of time, distance, or volume. Examples of application of Poisson probability distribution
      • The number of telephone calls received by an office during
      • a given day
      ii) The number of defects in a five-foot-long iron rod.
    • Jan 2009 Poisson Probability Distribution, X ~ P(  ) The probability of x occurrences in an interval is where  is the mean number of occurrences in that interval. ( per unit time or per unit area) Mean and Variance:
    • Jan 2009
      • Carry out experiment to estimate  that represents the
      • mean number of events that occur in one unit time/ space
      * The number of events X that occur in t units of time is counted and  is estimated. * If the number of events are independent and events cannot occur simultaneously, then X follows a Poisson distribution. A process that produces such events is a Poisson process Let  denote the mean number of events that occur in one unit of time.Let N T denote the number of events that are observed to occur in T units of time or space, then N T ~ P (  T), Poisson Process