Chap 4-1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chapter 4 Probability Statistics for Business and Economics 6 th Edition. - презентация

1 Chap 4-1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chapter 4 Probability Statistics for Business and Economics 6 th Edition

2 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-2 Chapter Goals After completing this chapter, you should be able to: Explain basic probability concepts and definitions Use a Venn diagram or tree diagram to illustrate simple probabilities Apply common rules of probability Compute conditional probabilities Determine whether events are statistically independent Use Bayes Theorem for conditional probabilities

3 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-3 Important Terms Random Experiment – a process leading to an uncertain outcome Basic Outcome – a possible outcome of a random experiment Sample Space – the collection of all possible outcomes of a random experiment Event – any subset of basic outcomes from the sample space

4 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-4 Important Terms Intersection of Events – If A and B are two events in a sample space S, then the intersection, A B, is the set of all outcomes in S that belong to both A and B (continued) AB A B S

5 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-5 Important Terms A and B are Mutually Exclusive Events if they have no basic outcomes in common i.e., the set A B is empty (continued) A B S

6 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-6 Important Terms Union of Events – If A and B are two events in a sample space S, then the union, A U B, is the set of all outcomes in S that belong to either A or B (continued) AB The entire shaded area represents A U B S

7 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-7 Important Terms Events E 1, E 2, … E k are Collectively Exhaustive events if E 1 U E 2 U... U E k = S i.e., the events completely cover the sample space The Complement of an event A is the set of all basic outcomes in the sample space that do not belong to A. The complement is denoted (continued) A S

8 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-8 Examples Let the Sample Space be the collection of all possible outcomes of rolling one die: S = [1, 2, 3, 4, 5, 6] Let A be the event Number rolled is even Let B be the event Number rolled is at least 4 Then A = [2, 4, 6] and B = [4, 5, 6]

9 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-9 (continued) Examples S = [1, 2, 3, 4, 5, 6] A = [2, 4, 6] B = [4, 5, 6] Complements: Intersections: Unions:

10 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-10 Mutually exclusive: A and B are not mutually exclusive The outcomes 4 and 6 are common to both Collectively exhaustive: A and B are not collectively exhaustive A U B does not contain 1 or 3 (continued) Examples S = [1, 2, 3, 4, 5, 6] A = [2, 4, 6] B = [4, 5, 6]

11 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-11 Probability Probability – the chance that an uncertain event will occur (always between 0 and 1) 0 P(A) 1 For any event A Certain Impossible.5 1 0

12 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-12 Assessing Probability There are three approaches to assessing the probability of an uncertain event: 1. classical probability Assumes all outcomes in the sample space are equally likely to occur

13 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-13 Counting the Possible Outcomes Use the Combinations formula to determine the number of combinations of n things taken k at a time where n! = n(n-1)(n-2)…(1) 0! = 1 by definition

14 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-14 Assessing Probability Three approaches (continued) 2. relative frequency probability the limit of the proportion of times that an event A occurs in a large number of trials, n 3. subjective probability an individual opinion or belief about the probability of occurrence

15 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-15 Probability Postulates 1. If A is any event in the sample space S, then 2. Let A be an event in S, and let O i denote the basic outcomes. Then (the notation means that the summation is over all the basic outcomes in A) 3.P(S) = 1

16 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-16 Probability Rules The Complement rule: The Addition rule: The probability of the union of two events is

17 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-17 A Probability Table B A Probabilities and joint probabilities for two events A and B are summarized in this table:

18 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-18 Addition Rule Example Consider a standard deck of 52 cards, with four suits: Let event A = card is an Ace Let event B = card is from a red suit

19 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-19 P(Red U Ace) = P(Red) + P(Ace) - P(Red Ace) = 26/52 + 4/52 - 2/52 = 28/52 Dont count the two red aces twice! Black Color Type Red Total Ace 224 Non-Ace Total (continued) Addition Rule Example

20 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-20 A conditional probability is the probability of one event, given that another event has occurred: The conditional probability of A given that B has occurred The conditional probability of B given that A has occurred Conditional Probability

21 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-21 What is the probability that a car has a CD player, given that it has AC ? i.e., we want to find P(CD | AC) Conditional Probability Example Of the cars on a used car lot, 70% have air conditioning (AC) and 40% have a CD player (CD). 20% of the cars have both.

22 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-22 Conditional Probability Example No CDCDTotal AC No AC Total Of the cars on a used car lot, 70% have air conditioning (AC) and 40% have a CD player (CD). 20% of the cars have both. (continued)

23 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-23 Conditional Probability Example No CDCDTotal AC No AC Total Given AC, we only consider the top row (70% of the cars). Of these, 20% have a CD player. 20% of 70% is 28.57%. (continued)

24 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-24 Multiplication Rule Multiplication rule for two events A and B: also

25 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-25 Multiplication Rule Example P(Red Ace) = P(Red| Ace)P(Ace) Black Color Type Red Total Ace 224 Non-Ace Total 26 52

26 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-26 Statistical Independence Two events are statistically independent if and only if: Events A and B are independent when the probability of one event is not affected by the other event If A and B are independent, then if P(B)>0 if P(A)>0

27 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-27 Statistical Independence Example No CDCDTotal AC No AC Total Of the cars on a used car lot, 70% have air conditioning (AC) and 40% have a CD player (CD). 20% of the cars have both. Are the events AC and CD statistically independent?

28 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-28 Statistical Independence Example No CDCDTotal AC No AC Total (continued) P(AC CD) = 0.2 P(AC) = 0.7 P(CD) = 0.4 P(AC)P(CD) = (0.7)(0.4) = 0.28 P(AC CD) = 0.2 P(AC)P(CD) = 0.28 So the two events are not statistically independent

29 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-29 Bivariate Probabilities B1B1 B2B2...BkBk A1A1 P(A 1 B 1 )P(A 1 B 2 )... P(A 1 B k ) A2A2 P(A 2 B 1 )P(A 2 B 2 )... P(A 2 B k ) AhAh P(A h B 1 )P(A h B 2 )... P(A h B k ) Outcomes for bivariate events:

30 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-30 Joint and Marginal Probabilities The probability of a joint event, A B: Computing a marginal probability: Where B 1, B 2, …, B k are k mutually exclusive and collectively exhaustive events

31 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-31 Marginal Probability Example P(Ace) Black Color Type Red Total Ace 224 Non-Ace Total 26 52

32 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-32 Using a Tree Diagram Has AC Does not have AC Has CD Does not have CD Has CD Does not have CD P(AC)=.7 P(AC)=.3 P(AC CD) =.2 P(AC CD) =.5 P(AC CD) =.1 P(AC CD) =.2 All Cars Given AC or no AC:

33 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-33 Odds The odds in favor of a particular event are given by the ratio of the probability of the event divided by the probability of its complement The odds in favor of A are

34 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-34 Odds: Example Calculate the probability of winning if the odds of winning are 3 to 1: Now multiply both sides by 1 – P(A) and solve for P(A): 3 x (1- P(A)) = P(A) 3 – 3P(A) = P(A) 3 = 4P(A) P(A) = 0.75

35 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-35 Overinvolvement Ratio The probability of event A 1 conditional on event B 1 divided by the probability of A 1 conditional on activity B 2 is defined as the overinvolvement ratio: An overinvolvement ratio greater than 1 implies that event A 1 increases the conditional odds ration in favor of B 1 :

36 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-36 Bayes Theorem where: E i = i th event of k mutually exclusive and collectively exhaustive events A = new event that might impact P(E i )

37 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-37 Bayes Theorem Example A drilling company has estimated a 40% chance of striking oil for their new well. A detailed test has been scheduled for more information. Historically, 60% of successful wells have had detailed tests, and 20% of unsuccessful wells have had detailed tests. Given that this well has been scheduled for a detailed test, what is the probability that the well will be successful?

38 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-38 Let S = successful well U = unsuccessful well P(S) =.4, P(U) =.6 (prior probabilities) Define the detailed test event as D Conditional probabilities: P(D|S) =.6 P(D|U) =.2 Goal is to find P(S|D) Bayes Theorem Example (continued)

39 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-39 Bayes Theorem Example (continued) Apply Bayes Theorem: So the revised probability of success (from the original estimate of.4), given that this well has been scheduled for a detailed test, is.667

40 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 4-40 Chapter Summary Defined basic probability concepts Sample spaces and events, intersection and union of events, mutually exclusive and collectively exhaustive events, complements Examined basic probability rules Complement rule, addition rule, multiplication rule Defined conditional, joint, and marginal probabilities Reviewed odds and the overinvolvement ratio Defined statistical independence Discussed Bayes theorem