1. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Chapter Discrete Probability Distributions 6

2. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Section Discrete Random Variables 6.1

3. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Objectives 1.Distinguish between discrete and continuous random variables 2.Identify discrete probability distributions 3.Construct probability histograms 4.Compute and interpret the mean of a discrete random variable 5.Interpret the mean of a discrete random 6.Compute the standard deviation of a discrete random variable 6-3

4. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Objective 1 Distinguish between Discrete and Continuous Random Variables 6-4

5. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-5 A random variable is a numerical measure of the outcome from a probability experiment, so its value is determined by chance. Random variables are denoted using letters such as X.

6. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-6 A discrete random variable has either a finite or countable number of values. The values of a discrete random variable can be plotted on a number line with space between each point.

7. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-7 A continuous random variable has infinitely many values. The values of a continuous random variable can be plotted on a line in an uninterrupted fashion.

8. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-8 Determine whether the following random variables are discrete or continuous. State possible values for the random variable. (a)The number of light bulbs that burn out in a room of 10 light bulbs in the next year. (b) The number of leaves on a randomly selected oak tree. (c) The length of time between calls to 911. EXAMPLEDistinguishing Between Discrete and Continuous Random Variables Discrete; x = 0, 1, 2, …, 10 Discrete; x = 0, 1, 2, … Continuous; t > 0

9. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Objective 2 Identify Discrete Probability Distributions 6-9

10. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-10 A probability distribution provides the possible values of the random variable X and their corresponding probabilities. A probability distribution can be in the form of a table, graph or mathematical formula.

11. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-11 The table to the right shows the probability distribution for the random variable X, where X represents the number of movies streamed on Netflix each month. xP(x)P(x) 00.06 10.58 20.22 30.10 40.03 50.01 EXAMPLEA Discrete Probability Distribution

12. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Rules for a Discrete Probability Distribution Let P(x) denote the probability that the random variable X equals x; then 1. Σ P(x) = 1 2. 0 ≤ P(x) ≤ 1 Rules for a Discrete Probability Distribution Let P(x) denote the probability that the random variable X equals x; then 1. Σ P(x) = 1 2. 0 ≤ P(x) ≤ 1 6-12

13. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-13 EXAMPLE Identifying Probability Distributions xP(x)P(x) 00.16 10.18 20.22 30.10 40.30 50.01 Is the following a probability distribution? No. Σ P(x) = 0.97

14. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-14 EXAMPLE Identifying Probability Distributions xP(x)P(x) 00.16 10.18 20.22 30.10 40.30 5– 0.01 Is the following a probability distribution? No. P(x = 5) = –0.01

15. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-15 EXAMPLE Identifying Probability Distributions xP(x)P(x) 00.16 10.18 20.22 30.10 40.30 50.04 Is the following a probability distribution? Yes. Σ P(x) = 1

16. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Objective 3 Construct Probability Histograms 6-16

17. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-17 A probability histogram is a histogram in which the horizontal axis corresponds to the value of the random variable and the vertical axis represents the probability of that value of the random variable.

18. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-18 Draw a probability histogram of the probability distribution to the right, which represents the number of movies streamed on Netflix each month. EXAMPLEDrawing a Probability Histogram xP(x)P(x) 00.06 10.58 20.22 30.10 40.03 50.01

19. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Objective 4 Compute and Interpret the Mean of a Discrete Random Variable 6-19

20. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. The Mean of a Discrete Random Variable The mean of a discrete random variable is given by the formula where x is the value of the random variable and P(x) is the probability of observing the value x. The Mean of a Discrete Random Variable The mean of a discrete random variable is given by the formula where x is the value of the random variable and P(x) is the probability of observing the value x. 6-20

21. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-21 Compute the mean of the probability distribution to the right, which represents the number of DVDs a person rents from a video store during a single visit. EXAMPLEComputing the Mean of a Discrete Random Variable xP(x)P(x) 00.06 10.58 20.22 30.10 40.03 50.01

22. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Interpretation of the Mean of a Discrete Random Variable Suppose an experiment is repeated n independent times and the value of the random variable X is recorded. As the number of repetitions of the experiment increases, the mean value of the n trials will approach μ X, the mean of the random variable X. In other words, let x 1 be the value of the random variable X after the first experiment, x 2 be the value of the random variable X after the second experiment, and so on. Then The difference between and μ X gets closer to 0 as n increases. Interpretation of the Mean of a Discrete Random Variable Suppose an experiment is repeated n independent times and the value of the random variable X is recorded. As the number of repetitions of the experiment increases, the mean value of the n trials will approach μ X, the mean of the random variable X. In other words, let x 1 be the value of the random variable X after the first experiment, x 2 be the value of the random variable X after the second experiment, and so on. Then The difference between and μ X gets closer to 0 as n increases. 6-22

23. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-23 The following data represent the number of DVDs rented by 100 randomly selected customers in a single visit. Compute the mean number of DVDs rented.

24. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-24

25. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-25 As the number of trials of the experiment increases, the mean number of rentals approaches the mean of the probability distribution.

26. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Objective 5 Compute and Interpret the Mean of a Discrete Random Variable 6-26

27. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-27 Because the mean of a random variable represents what we would expect to happen in the long run, it is also called the expected value, E(X), of the random variable.

28. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-28 EXAMPLEComputing the Expected Value of a Discrete Random Variable A term life insurance policy will pay a beneficiary a certain sum of money upon the death of the policy holder. These policies have premiums that must be paid annually. Suppose a life insurance company sells a $250,000 one year term life insurance policy to a 49-year-old female for $530. According to the National Vital Statistics Report, Vol. 47, No. 28, the probability the female will survive the year is 0.99791. Compute the expected value of this policy to the insurance company. xP(x)P(x) 5300.99791 530 – 250,000 = -249,470 0.00209 Survives Does not survive E(X) = 530(0.99791) + (-249,470)(0.00209) = $7.50

29. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Objective 6 Compute the Standard Deviation of a Discrete Random Variable 6-29

30. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Standard Deviation of a Discrete Random Variable The standard deviation of a discrete random variable X is given by where x is the value of the random variable, μ X is the mean of the random variable, and P(x) is the probability of observing a value of the random variable. Standard Deviation of a Discrete Random Variable The standard deviation of a discrete random variable X is given by where x is the value of the random variable, μ X is the mean of the random variable, and P(x) is the probability of observing a value of the random variable. 6-30

31. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-31 Compute the variance and standard deviation of the following probability distribution which represents the number of DVDs a person rents from a video store during a single visit. Remember, the mean that we found was 1.49. EXAMPLEComputing the Variance and Standard Deviation of a Discrete Random Variable xP(x)P(x) 00.06 10.58 20.22 30.10 40.03 50.01

32. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. 6-32 x μ P(x)P(x) 01.49–1.492.2201 0.060.133206 11.49 –0.490.24010.580.139258 21.49 0.510.26010.220.057222 31.49 1.512.28010.10.22801 41.49 2.516.30010.030.189003 51.49 3.5112.32010.010.123201 EXAMPLEComputing the Variance and Standard Deviation of a Discrete Random Variable