1. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Chapter Probability 5

2. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Section Probability Rules 5.1

3. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Objectives 1.Apply the rules of probabilities 2.Compute and interpret probabilities using the empirical method 3.Compute and interpret probabilities using the classical method 4.Use simulation to obtain data based on probabilities 5.Recognize and interpret subjective probabilities 5-3

4. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Probability is a measure of the likelihood of a random phenomenon or chance behavior. Probability describes the long-term proportion with which a certain outcome will occur in situations with short-term uncertainty. Use the probability applet to simulate flipping a coin 100 times. Plot the proportion of heads against the number of flips. Repeat the simulation. 5-4

5. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Probability deals with experiments that yield random short-term results or outcomes, yet reveal long-term predictability. The long-term proportion in which a certain outcome is observed is the probability of that outcome. 5-5

6. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. The Law of Large Numbers As the number of repetitions of a probability experiment increases, the proportion with which a certain outcome is observed gets closer to the probability of the outcome. The Law of Large Numbers As the number of repetitions of a probability experiment increases, the proportion with which a certain outcome is observed gets closer to the probability of the outcome. 5-6

7. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. In probability, an experiment is any process that can be repeated in which the results are uncertain. The sample space, S, of a probability experiment is the collection of all possible outcomes. An event is any collection of outcomes from a probability experiment. An event may consist of one outcome or more than one outcome. We will denote events with one outcome, sometimes called simple events, e i. In general, events are denoted using capital letters such as E. 5-7

8. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Consider the probability experiment of having two children. (a) Identify the outcomes of the probability experiment. (b) Determine the sample space. (c) Define the event E = “have one boy”. EXAMPLEIdentifying Events and the Sample Space of a Probability Experiment (a) e 1 = boy, boy, e 2 = boy, girl, e 3 = girl, boy, e 4 = girl, girl (b) {(boy, boy), (boy, girl), (girl, boy), (girl, girl)} (c) {(boy, girl), (girl, boy)} 5-8

9. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Objective 1 Apply the Rules of Probabilities 5-9

10. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Rules of probabilities 1.The probability of any event E, P(E), must be greater than or equal to 0 and less than or equal to 1. That is, 0 ≤ P(E) ≤ 1. 2.The sum of the probabilities of all outcomes must equal 1. That is, if the sample space S = {e 1, e 2, …, e n }, then P(e 1 ) + P(e 2 ) + … + P(e n ) = 1 Rules of probabilities 1.The probability of any event E, P(E), must be greater than or equal to 0 and less than or equal to 1. That is, 0 ≤ P(E) ≤ 1. 2.The sum of the probabilities of all outcomes must equal 1. That is, if the sample space S = {e 1, e 2, …, e n }, then P(e 1 ) + P(e 2 ) + … + P(e n ) = 1 5-10

11. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. A probability model lists the possible outcomes of a probability experiment and each outcome’s probability. A probability model must satisfy rules 1 and 2 of the rules of probabilities. 5-11

12. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. EXAMPLE A Probability Model In a bag of peanut M&M milk chocolate candies, the colors of the candies can be brown, yellow, red, blue, orange, or green. Suppose that a candy is randomly selected from a bag. The table shows each color and the probability of drawing that color. Verify this is a probability model. ColorProbability Brown0.12 Yellow0.15 Red0.12 Blue0.23 Orange0.23 Green0.15 All probabilities are between 0 and 1, inclusive. Because 0.12 + 0.15 + 0.12 + 0.23 + 0.23 + 0.15 = 1, rule 2 (the sum of all probabilities must equal 1) is satisfied. 5-12

13. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. If an event is a certainty, the probability of the event is 1. If an event is impossible, the probability of the event is 0. An unusual event is an event that has a low probability of occurring. 5-13

14. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Objective 2 Compute and Interpret Probabilities Using the Empirical Method 5-14

15. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Approximating Probabilities Using the Empirical Approach The probability of an event E is approximately the number of times event E is observed divided by the number of repetitions of the experiment. P(E) ≈ relative frequency of E Approximating Probabilities Using the Empirical Approach The probability of an event E is approximately the number of times event E is observed divided by the number of repetitions of the experiment. P(E) ≈ relative frequency of E 5-15

16. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Pass the Pigs TM is a Milton- Bradley game in which pigs are used as dice. Points are earned based on the way the pig lands. There are six possible outcomes when one pig is tossed. A class of 52 students rolled pigs 3,939 times. The number of times each outcome occurred is recorded in the table at right EXAMPLE Building a Probability Model OutcomeFrequency Side with no dot1344 Side with dot1294 Razorback767 Trotter365 Snouter137 Leaning Jowler32 5-16

17. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. EXAMPLE Building a Probability Model (c)Would it be unusual to throw a “Leaning Jowler”? 5-17 OutcomeFrequency Side with no dot1344 Side with dot1294 Razorback767 Trotter365 Snouter137 Leaning Jowler32 (a)Use the results of the experiment to build a probability model for the way the pig lands. (b)Estimate the probability that a thrown pig lands on the “side with dot”.

18. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. (a) OutcomeProbability Side with no dot Side with dot0.329 Razorback0.195 Trotter0.093 Snouter0.035 Leaning Jowler0.008 5-18

19. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. (a) OutcomeProbability Side with no dot0.341 Side with dot0.329 Razorback0.195 Trotter0.093 Snouter0.035 Leaning Jowler0.008 (b)The probability a throw results in a “side with dot” is 0.329. In 1000 throws of the pig, we would expect about 329 to land on a “side with dot”. 5-19

20. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. (a) OutcomeProbability Side with no dot0.341 Side with dot0.329 Razorback0.195 Trotter0.093 Snouter0.035 Leaning Jowler0.008 (c)A “Leaning Jowler” would be unusual. We would expect in 1000 throws of the pig to obtain “Leaning Jowler” about 8 times. 5-20

21. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Objective 3 Compute and Interpret Probabilities Using the Classical Method 5-21

22. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. The classical method of computing probabilities requires equally likely outcomes. An experiment is said to have equally likely outcomes when each simple event has the same probability of occurring. 5-22

23. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Computing Probability Using the Classical Method If an experiment has n equally likely outcomes and if the number of ways that an event E can occur is m, then the probability of E, P(E) is Computing Probability Using the Classical Method If an experiment has n equally likely outcomes and if the number of ways that an event E can occur is m, then the probability of E, P(E) is 5-23

24. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Computing Probability Using the Classical Method So, if S is the sample space of this experiment, where N(E) is the number of outcomes in E, and N(S) is the number of outcomes in the sample space. Computing Probability Using the Classical Method So, if S is the sample space of this experiment, where N(E) is the number of outcomes in E, and N(S) is the number of outcomes in the sample space. 5-24

25. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. EXAMPLE Computing Probabilities Using the Classical Method Suppose a “fun size” bag of M&Ms contains 9 brown candies, 6 yellow candies, 7 red candies, 4 orange candies, 2 blue candies, and 2 green candies. Suppose that a candy is randomly selected. (a) What is the probability that it is yellow? (b) What is the probability that it is blue? (c) Comment on the likelihood of the candy being yellow versus blue. 5-25

26. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. EXAMPLE Computing Probabilities Using the Classical Method (a)There are a total of 9 + 6 + 7 + 4 + 2 + 2 = 30 candies, so N(S) = 30. (b) P(blue) = 2/30 = 0.067. (c) Since P(yellow) = 6/30 and P(blue) = 2/30, selecting a yellow is three times as likely as selecting a blue. 5-26

27. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Objective 4 Use Simulation to Obtain Data Based on Probabilities 5-27

28. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Use the probability applet to simulate throwing a 6- sided die 100 times. Approximate the probability of rolling a 4. How does this compare to the classical probability? Repeat the exercise for 1000 throws of the die. EXAMPLEUsing Simulation 5-28

29. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Objective 5 Recognize and Interpret Subjective Probabilities 5-29

30. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. The subjective probability of an outcome is a probability obtained on the basis of personal judgment. For example, an economist predicting there is a 20% chance of recession next year would be a subjective probability. 5-30

31. Copyright © 2013, 2010 and 2007 Pearson Education, Inc. In his fall 1998 article in Chance Magazine, (“A Statistician Reads the Sports Pages,” pp. 17-21,) Hal Stern investigated the probabilities that a particular horse will win a race. He reports that these probabilities are based on the amount of money bet on each horse. When a probability is given that a particular horse will win a race, is this empirical, classical, or subjective probability? EXAMPLEEmpirical, Classical, or Subjective Probability Subjective because it is based upon people’s feelings about which horse will win the race. The probability is not based on a probability experiment or counting equally likely outcomes. 5-31