Discrete Probability Distributions
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1 Discrete Probability Distributions Chapter 6 Copyright 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.
2 Learning Objectives LO6-1 Identify the characteristics of a probability distribution. LO6-2 Distinguish between discrete and continuous random variables. LO6-3 Compute the mean, variance, and standard deviation of a discrete probability distribution. LO6-4 Explain the assumptions of the binomial distribution and apply it to calculate probabilities. LO6-5 Hypergeometric distribution (excluded) LO6-6 Explain the assumptions of the Poisson distribution and apply it to calculate probabilities. 6-2
3 LO6-1 Identify the characteristics of a probability distribution. What is a Probability Distribution? PROBABILITY DISTRIBUTION A listing of all the outcomes of an experiment and the probability associated with each outcome. 6-3
4 LO6-1 Characteristics of a Probability Distribution 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
5 LO6-1 Probability Distribution - Example Experiment: Toss a coin three times. Observe the number of heads. The possible experimental outcomes are: zero heads, one head, two heads, and three heads. What is the probability distribution for the number of heads? 6-5
6 LO6-1 Probability Distribution: Number of Heads in 3 Tosses of a Coin 6-6
7 LO6-2 Distinguish between discrete and continuous random variables. Random Variables RANDOM VARIABLE A quantity resulting from an experiment that, by chance, can assume different values. 6-7
8 LO6-2 Types of Random Variables DISCRETE RANDOM VARIABLE A random variable that can assume only certain clearly separated values. It is usually the result of counting something. CONTINUOUS RANDOM VARIABLE A random variable that can assume an infinite number of values within a given range. It is usually the result of some type of measurement. 6-8
9 LO6-2 Discrete Random Variable DISCRETE RANDOM VARIABLE A random variable that can assume only certain clearly separated values. It is usually the result of counting something. EXAMPLES: The number of students in a class The number of children in a family The number of cars entering a carwash in a hour The number of home mortgages approved by Coastal Federal Bank last week 6-9
10 LO6-2 Continuous Random Variable CONTINUOUS RANDOM VARIABLE A random variable that can assume an infinite number of values within a given range. It is usually the result of some type of measurement EXAMPLES: The length of each song on the latest Tim McGraw CD The weight of each student in this class The amount of money earned by each player in the National Football League 6-10
11 LO6-3 Compute the mean, variance, and standard deviation of a discrete probability distribution. The Mean of a Discrete Probability Distribution The mean is a typical value used to represent the central location of a probability distribution. The mean of a probability distribution is also referred to as its expected value. 6-11
12 LO6-3 The Mean of a Discrete Probability Distribution - 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. 6-12
13 LO6-3 The Mean of a Discrete Probability Distribution - Example 6-13
14 LO6-3 The Variance and Standard Deviation of a Discrete Probability Distribution Measures the amount of spread in a distribution. The computational steps are: 1. Subtract the mean from each value, and square this difference. 2. Multiply each squared difference by its probability. 3. Sum the resulting products to arrive at the variance. 6-14
15 The Variance and Standard Deviation of a Discrete Probability Distribution - Example LO
16 LO6-4 Explain the assumptions of the binomial distribution and apply it to calculate probabilities. Binomial Probability Distribution A widely occurring discrete probability distribution Characteristics of a binomial probability distribution: 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. 6-16
17 LO6-4 Characteristics of a Binomial Probability Experiment The outcome of each trial is classified into one of two mutually exclusive categories a success or a failure. The random variable, x, is the number of successes in a fixed number of trials. The probability of success and failure stay the same for each trial. The trials are independent, meaning that the outcome of one trial does not affect the outcome of any other trial. 6-17
18 LO6-4 Binomial Probability Formula 6-18
19 LO6-4 Binomial Probability - Example There are five flights daily from Pittsburgh via US Airways into the Bradford Regional Airport. Suppose the probability that any flight arrives late is What is the probability that none of the flights are late today? Recall: 0! = 1, and, any variable with a 0 exponent is equal to one. 6-19
20 LO6-4 Binomial Distribution Probability The probabilities for each value of the random variable, number of late flights (0 through 5), can be calculated to create the entire binomial probability distribution. 6-20
21 LO6-4 Mean and Variance of a Binomial Distribution Knowing the number of trials, n, and the probability of a success,, for a binomial distribution, we can compute the mean and variance of the distribution. 6-21
22 LO6-4 Mean and Variance of a Binomial Distribution - Example For the example regarding the number of late flights, recall that =.20 and n = 5. What is the average number of late flights? What is the variance of the number of late flights? 6-22
23 LO6-4 Mean and Variance of a Binomial Distribution Example Using the general formulas for discrete probability distributions: 6-23
24 LO6-4 Binomial Probability Distributions Tables Binomial probability distributions can be listed in tables. The calculations have already been done. In the table below, the binomial distributions for n=6 trials, and the different values of the probability of success are listed. 6-24
25 LO6-4 Binomial Probability Distribution- Excel Example Using the Excel Function: Binom.dist(x,n,,false) 6-25
26 LO6-4 Binomial Shapes or Skewness for Varying and n=10 The shape of a binomial distribution changes as n and change. 6-26
27 LO6-4 Binomial Shapes or Skewness for Constant and Varying n 6-27
28 LO6-4 Binomial Probability Distributions Excel Example A study by the Illinois Department of Transportation showed that 76.2 percent of front seat occupants used seat belts. If a sample of 12 cars traveling on a highway are selected, the binomial probability distribution of cars with front seat occupants using seat belts can be calculated as shown. 6-28
29 LO6-4 Binomial Probability Distributions Excel Example What is the probability the front seat occupants in exactly 7 of the 12 vehicles are wearing seat belts? 6-29
30 LO6-4 Cumulative Binomial Probability Distributions Excel Example What is the probability the front seat occupants in at least 7 of the 12 vehicles are wearing seat belts? 6-30
31 LO6-6 Explain the assumptions of the Poisson distribution and apply it to calculate probabilities. Poisson Probability Distribution The Poisson probability distribution describes the number of times some event occurs during a specified interval. The interval may be time, distance, area, or volume. Assumptions of the Poisson Distribution: The probability is proportional to the length of the interval. The intervals are independent. 6-31
32 LO6-6 Poisson Probability Distribution The Poisson probability distribution is characterized by the number of times an event happens during some interval or continuum. Examples: The number of misspelled words per page in a newspaper The number of calls per hour received by Dyson Vacuum Cleaner Company The number of vehicles sold per day at Hyatt Buick GMC in Durham, North Carolina The number of goals scored in a college soccer game 6-32
33 LO6-6 Poisson Probability Distribution The Poisson distribution can be described mathematically by the formula: 6-33
34 LO6-6 Poisson Probability Distribution The mean number of successes, μ, can be determined in Poisson situations by n, where n is the number of trials and the probability of a success. The variance of the Poisson distribution is also equal to n. 6-34
35 LO6-6 Poisson Probability Distribution Example Assume baggage is rarely lost by Northwest Airlines. Suppose a random sample of 1,000 flights shows a total of 300 bags were lost. Thus, the arithmetic mean number of lost bags per flight is 0.3 (300/1,000). If the number of lost bags per flight follows a Poisson distribution with u = 0.3, find the probability of not losing any bags. 6-35
36 LO6-6 Poisson Probability Distribution Table Example Recall from the previous illustration that the number of lost bags follows a Poisson distribution with a mean of 0.3. A table can be used to find the probability that no bags will be lost on a particular flight. What is the probability no bag will be lost on a particular flight? 6-36
37 LO6-6 More About the Poisson Probability Distribution The Poisson probability distribution is always positively skewed and the random variable has no specific upper limit. The Poisson distribution for the lost bags illustration, where µ=0.3, is highly skewed. As µ becomes larger, the Poisson distribution becomes more symmetrical. 6-37
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