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Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

Dec 23, 2015

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Page 1: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.
Page 2: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

Copyright © 2011 Pearson Education, Inc.

Probability

Chapter 7

Page 3: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.1 From Data to Probability

In a call center, what is the probability that an agent answers an easy call?

An easy call can be handled by a first-tier agent; a hard call needs further assistance

Two possible outcomes: easy and hard calls

Are they equally likely?

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Page 4: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.1 From Data to Probability

Probability = Long Run Relative Frequency

Keep track of calls (1 = easy call; 0 = hard call)

Graph the accumulated relative frequency of easy calls

In the long run, the accumulated relative frequency converges to a constant (probability)

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Page 5: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.1 From Data to Probability

The Law of Large Numbers (LLN)

The relative frequency of an outcome converges to a number, the probability of the outcome, as the number of observed outcomes increases.

Note: The pattern must converge for LLN to apply.

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Page 6: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.1 From Data to Probability

The Accumulated Relative Frequency of Easy Calls Converges to 70%

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Page 7: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.2 Rules for Probability

Sample Space

Set of all possible outcomes

Denoted by S; S = {easy, hard}

Subsets of samples spaces are events; denoted as A, B, etc.

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Page 8: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.2 Rules for Probability

Venn Diagrams

The probability of an event A is denoted as P(A).

Venn diagrams are graphs for depicting the relationships among events

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Page 9: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.2 Rules for Probability

Rule 1: Since S is the set of all possible outcomes, P(S) = 1

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Page 10: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.2 Rules for Probability

Rule 2: For any event A, 0 ≤ P(A) ≤ 1.

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Page 11: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.2 Rules for Probability

Rule 3: Addition Rule for Disjoint Events

Disjoint events are mutually exclusive; i.e., they have no outcomes in common.

The union of two events is the collection of outcomes in A, in B, or in both (A or B)

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Page 12: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.2 Rules for Probability

Rule 3: Addition Rule for Disjoint EventsIf A and B are disjoint events, then P (A or B) = P(A) + P(B).

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Page 13: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.2 Rules for Probability

Rule 3: Addition Rule for Disjoint Events

Extends to more than two events

P (E1 or E2 or … or Ek) =

P(E1) + P(E2) + … + P(Ek)

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Page 14: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.2 Rules for Probability

Rule 4: Complement Rule

The complement of event A consists of the outcomes in S but not in A

Denoted as Ac

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Page 15: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.2 Rules for Probability

Rule 4: Complement Rule: P(A) = 1 – P(Ac)The probability of an event is one minus the

probability of its complement.

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Page 16: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.2 Rules for Probability

Rule 5: General Addition Rule

The intersection of A and B contains the outcomes in both A and B

Denoted as A ∩ B read “A and B”

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Page 17: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.2 Rules for Probability

Rule 5: General Addition RuleP (A or B) = P(A) + P(B) – P (A and B).

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Page 18: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.2 Rules for Probability

An Example – Movie Schedule

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Page 19: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.2 Rules for Probability

What’s the probability that the next customer buys a ticket for a movie that starts at 9 PM or is a drama?

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Page 20: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.2 Rules for Probability

What’s the probability that the next customer buys a ticket for a movie that starts at 9 PM or is a drama?

Use the General Addition Rule:P(A or B) = P(9 PM or Drama)

= 3/6 + 3/6 – 2/6 = 2/3

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Page 21: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.3 Independent Events

Definitions

Two events are independent if the occurrence of one does not affect the chances for the occurrence of the other

Events that are not independent are called dependent

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Page 22: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.3 Independent Events

Multiplication Rule

Two events A and B are independent if the probability that both A and B occur is the product of the probabilities of the two events.

P (A and B) = P(A) X P(B)

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Page 23: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

4M Example 7.1: MANAGING A PROCESS

Motivation

What is the probability that a breakdown on an assembly line will occur in the next five days, interfering with the completion of an order?

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Page 24: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

4M Example 7.1: MANAGING A PROCESS

Method

Past data indicates a 95% chance that the assembly line runs a full day without breaking down.

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Page 25: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

4M Example 7.1: MANAGING A PROCESS

Mechanics

Assuming days are independent, use the multiplication rule to find

P (OK for 5 days) = 0.955 = 0.774

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Page 26: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

4M Example 7.1: MANAGING A PROCESS

Mechanics

Use the complement rule to find

P (breakdown during 5 days) = 1 - P(OK for 5 days)= 1- 0.774 = 0.226

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Page 27: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

4M Example 7.1: MANAGING A PROCESS

Message

The probability that a breakdown interrupts production in the next five days is 0.226. It is wise to warn the customer that delivery may be delayed.

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Page 28: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.3 Independent Events

Boole’s Inequality

Also known as Bonferroni’s inequality

The probability of a union is less than or equal to the sum of the probabilities of the events.

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Page 29: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

7.3 Independent Events

Boole’s Inequality

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Page 30: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

Best Practices

Make sure that your sample space includes all of the possibilities.

Include all of the pieces when describing an event.

Check that the probabilities assigned to all of the possible outcomes add up to 1.

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Page 31: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

Best Practices (Continued)

Only add probabilities of disjoint events.

Be clear about independence.

Only multiply probabilities of independent events.

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Page 32: Copyright © 2011 Pearson Education, Inc. Probability Chapter 7.

Pitfalls

Do not multiply probabilities of dependent events.

Avoid assigning the same probability to every outcome.

Do not confuse independent events with disjoint events.

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