Discrete Probability Distributions
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1 Discrete Probability Distributions Chapter 6 McGraw-Hill/Irwin Copyright 2010 by The McGraw-Hill Companies, Inc. All rights reserved.
2 GOALS Define the terms probability distribution and random variable. 2. Distinguish between discrete and continuous probability distributions. 3. Calculate the mean, variance, and standard deviation of a discrete probability distribution. 4. Describe the characteristics of and compute probabilities using the binomial probability distribution. 5. Describe the characteristics of and compute probabilities using the hypergeometric probability distribution. 6. Describe the characteristics of and compute probabilities using the Poisson probability distribution.
3 What is a Probability Distribution? PROBABILITY DISTRIBUTION A listing of all the outcomes of an experiment and the probability associated with each outcome. Experiment: Toss a coin three times. Observe the number of heads. The possible results are: Zero heads, One head, Two heads, and Three heads. 6-3 What is the probability distribution for the number of heads?
4 Characteristics of a Probability Distribution CHARACTERISTICS OF A PROBABILITY DISTRIBUTION 1. The probability of a particular outcome is between 0 and 1 inclusive. 2. The outcomes are mutually exclusive events. 3. The list is exhaustive. So the sum of the probabilities of the various events is equal to
5 6-5 Probability Distribution of Number of Heads Observed in 3 Tosses of a Coin
6 Random Variables RANDOM VARIABLE A quantity resulting from an experiment that, by chance, can assume different values. 6-6
7 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 can assume an infinite number of values within a given range. It is usually the result of some type of measurement 6-7
8 Discrete Random Variables DISCRETE RANDOM VARIABLE A random variable that can assume only certain clearly separated values. It is usually the result of counting something. EXAMPLES 1. The number of students in a class. 2. The number of children in a family. 3. The number of cars entering a carwash in a hour. 4. Number of home mortgages approved by Coastal Federal Bank last week. 6-8
9 Continuous Random Variables CONTINUOUS RANDOM VARIABLE 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 album. The weight of each student in this class. The temperature outside as you are reading this book. The amount of money earned by each of the more than 750 players currently on Major League Baseball team rosters. 6-9
10 The Mean of a Probability Distribution MEAN 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. 6-10
11 The Variance and Standard Deviation of a Probability Distribution VARIANCE AND STANDARD DEVIATION 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. The standard deviation is found by taking the positive square root of the variance. 6-11
12 Mean, Variance, and Standard Deviation of a 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 6-13 Mean of a Probability Distribution - Example
14 Variance and Standard Deviation of a Probability Distribution - Example
15 Binomial Probability Distribution A Widely occurring discrete probability distribution Characteristics of a Binomial Probability Distribution 1. There are only two possible outcomes on a particular trial of an experiment. 2. The outcomes are mutually exclusive, 3. The random variable is the result of counts. 4. Each trial is independent of any other trial 6-15
16 Binomial Probability Experiment 1. An outcome on each trial of an experiment is classified into one of two mutually exclusive categories a success or a failure. 2. The random variable counts the number of successes in a fixed number of trials. 3. The probability of success and failure stay the same for each trial. 4. The trials are independent, meaning that the outcome of one trial does not affect the outcome of any other trial. 6-16
17 6-17 Binomial Probability Formula
18 Binomial Probability - Example There are five flights daily from Pittsburgh via US Airways into the Bradford, Pennsylvania, Regional Airport. Suppose the probability that any flight arrives late is.20. What is the probability that none of the flights are late today? 6-18
19 6-19 Binomial Probability - Excel
20 6-20 Binomial Dist. Mean and Variance
21 Binomial Dist. Mean and Variance: 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? 6-21
22 6-22 Binomial Dist. Mean and Variance: Another Solution
23 Binomial Distribution - Table Five percent of the worm gears produced by an automatic, highspeed Carter-Bell milling machine are defective. What is the probability that out of six gears selected at random none will be defective? Exactly one? Exactly two? Exactly three? Exactly four? Exactly five? Exactly six out of six? 6-23
24 Binomial Distribution - MegaStat Five percent of the worm gears produced by an automatic, high-speed Carter-Bell milling machine are defective. What is the probability that out of six gears selected at random none will be defective? Exactly one? Exactly two? Exactly three? Exactly four? Exactly five? Exactly six out of six? 6-24
25 6-25 Binomial Shapes for Varying (n constant)
26 6-26 Binomial Shapes for Varying n ( constant)
27 Binomial Probability Distributions - Example A study by the Illinois Department of Transportation concluded that 76.2 percent of front seat occupants used seat belts. A sample of 12 vehicles is selected. What is the probability the front seat occupants in exactly 7 of the 12 vehicles are wearing seat belts? 6-27
28 Cumulative Binomial Probability Distributions - Example A study by the Illinois Department of Transportation concluded that 76.2 percent of front seat occupants used seat belts. A sample of 12 vehicles is selected. What is the probability the front seat occupants in at least 7 of the 12 vehicles are wearing seat belts? 6-28
29 6-29 Cumulative Binomial Probability Distributions - Excel
30 Hypergeometric Probability Distribution 1. An outcome on each trial of an experiment is classified into one of two mutually exclusive categories a success or a failure. 2. The probability of success and failure changes from trial to trial. 3. The trials are not independent, meaning that the outcome of one trial affects the outcome of any other trial Note: Use hypergeometric distribution if experiment is binomial, but sampling is without replacement from a finite population where n/n is more than 0.05
31 6-31 Hypergeometric Probability Distribution - Formula
32 Hypergeometric Probability Distribution - Example PlayTime Toys, Inc., employs 50 people in the Assembly Department. Forty of the employees belong to a union and ten do not. Five employees are selected at random to form a committee to meet with management regarding shift starting times. What is the probability that four of the five selected for the committee belong to a union? 6-32
33 Hypergeometric Probability Distribution - Example Here s what s given: N = 50 (number of employees) S = 40 (number of union employees) x = 4 (number of union employees selected) n = 5 (number of employees selected) What is the probability that four of the five selected for the committee belong to a union? 6-33
34 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 (1) The probability is proportional to the length of the interval. (2) The intervals are independent. 6-34
35 Poisson Probability Distribution The Poisson probability distribution is characterized by the number of times an event happens during some interval or continuum. Examples include: 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. 6-35
36 Poisson Probability Distribution The Poisson distribution can be described mathematically using the formula: 6-36
37 Poisson Probability Distribution The mean number of successes μ can be determined in binomial 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. 6-37
38 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. 6-38
39 Poisson Probability Distribution - Table Recall from the previous illustration that the number of lost bags follows a Poisson distribution with a mean of 0.3. Use Appendix B.5 to find the probability that no bags will be lost on a particular flight. What is the probability exactly one bag will be lost on a particular flight? 6-39
40 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. 6-40
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