Discrete Probability Distributions
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1 Discrete Probability Distributions Chapter 6 McGraw-Hill/Irwin Copyright 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
2 LO5 Describe and compute probabilities for a binomial distribution. 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 6-2
3 LO5 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. 6-3
4 LO5 Binomial Probability Formula 6-4
5 LO5 Binomial Probability - Example There are five flights daily from Pittsburgh via US Airways into the Bradford Regional Airport in PA. Suppose the probability that any flight arrives late is.20. What is the probability that none of the flights are late today? 6-5
6 LO5 Binomial Probability - Excel 6-6
7 LO5 Binomial Dist. Mean and Variance 6-7
8 Binomial Dist. Mean and Variance: Example LO5 For the example regarding the number of late flights, recall that π =.20 and n = 5. What is the average number of late flights? What is the variance of the number of late flights? 6-8
9 Binomial Dist. Mean and Variance: Another Solution LO5 6-9
10 LO5 Binomial Distribution - Table 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? 6-10
11 LO5 Binomial Shapes for Varying π (n constant) 6-11
12 Binomial Shapes for Varying n (π constant) LO5 6-12
13 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? LO5 6-13
14 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? LO5 6-14
15 Cumulative Binomial Probability Distributions - Excel LO5 6-15
16 LO7 Describe and compute probabilities for a Poisson distribution. 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. 6-16
17 LO7 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. 6-17
18 LO7 Poisson Probability Distribution The Poisson distribution can be described mathematically using the formula: 6-18
19 LO7 Poisson Probability Distribution The mean number of successes μ can be determined in Poisson situations by nπ, where n is the number of trials and π the probability of a success. The variance of the Poisson distribution is also equal to n π. 6-19
20 LO7 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. 6-20
21 LO7 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 no bag will be lost on a particular flight? 6-21
22 LO7 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. 6-22
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