Chap005Lind.ppt

A Survey of
Probability Concepts
Chapter 5
McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.

Learning Objectives
LO1 Define probability.
LO2 Explain the terms experiment, event, outcome,
LO3 Identify and apply the appropriate approach to
assigning probabilities,
LO4 Calculate probabilities using the rules of addition,
LO5 Define the term joint probability,
LO6 Calculate probabilities using the rules of
multiplication,
LO7 Define the term conditional probability,
LO8 Calculate probabilities using a contingency table,
LO9 Calculate a probability using Bayes’ theorem,
LO10 Determine the number of outcomes using the
appropriate principle of counting. 5-2

Probability
PROBABILITY A value between zero and one,
inclusive, describing the relative possibility
(chance or likelihood) an event will occur.
LO1 Define probability
5-3

Experiment, Outcome and Event
 An experiment is a process that leads to the occurrence of one and only one of
several possible observations.
 An outcome is the particular result of an experiment.
 An event is the collection of one or more outcomes of an experiment.
LO2 Explain the terms
experiment, event, outcome
5-4

Ways of Assigning Probability
There are three ways of assigning probability:
1. CLASSICAL PROBABILITY
Based on the assumption that the outcomes of an experiment
are equally likely.
2. EMPIRICAL PROBABILITY
The probability of an event happening is the fraction of the time
similar events happened in the past.
3. SUBJECTIVE CONCEPT OF PROBABILITY
The likelihood (probability) of a particular event happening that
is assigned by an individual based on whatever information is
available.
LO3 Identify and apply the appropriate
approach to assigning probabilities
5-5

Classical Probability
Consider an experiment of rolling a six-sided die. What is the
probability of the event “an even number of spots appear face
up”?
The possible outcomes are:
There are three “favorable” outcomes (a two, a four, and a six) in
the collection of six equally likely possible outcomes.
LO3
5-6

Mutually Exclusive Events and
Collectively Exhaustive Events
 Events are mutually exclusive if the occurrence of
any one event means that none of the others can
occur at the same time.
 Events are independent if the occurrence of one
event does not affect the occurrence of another.
Events are collectively exhaustive if at least one
of the events must occur when an experiment is
conducted.
 Events are collectively exhaustive if at least one
of the events must occur when an experiment is
conducted.
LO3
5-7

Empirical Probability
Empirical approach to probability is based on what is
called the Law of Large Numbers.
Key to establishing probabilities empirically – larger
number of observations provides a more accurate
estimate of the probability.
LO3
5-8

Empirical Probability - Example
On February 1, 2003, the Space Shuttle Columbia
exploded. This was the second disaster in 123 space
missions for NASA. On the basis of this information,
what is the probability that a future mission is
successfully completed?
98
.
0
123
121
flights
of
number
Total
flights
successful
of
Number
flight
successful
a
of
y
Probabilit



LO3
5-9

Subjective Probability - Example
 If there is little or no past experience or information on which to
base a probability, it may be arrived at subjectively.
 Illustrations of subjective probability are:
1. Estimating the likelihood the New England Patriots will play in the
Super Bowl next year.
2. Estimating the likelihood you will be married before the age of 30.
3. Estimating the likelihood the U.S. budget deficit will be reduced by
half in the next 10 years.
LO3
5-10

Summary of Types of
Probability
LO3
5-11

Rules of Addition for Computing Probabilities
Rules of Addition
 Special Rule of Addition - If two events
A and B are mutually exclusive, the
probability of one or the other event’s
occurring equals the sum of their
probabilities.
P(A or B) = P(A) + P(B)
 The General Rule of Addition - If A and
B are two events that are not mutually
exclusive, then P(A or B) is given by the
following formula:
P(A or B) = P(A) + P(B) - P(A and B)
LO4 Calculate probabilities using rules
of addition.
5-12

Addition Rule – Mutually Exclusive Events
Example
An automatic Shaw machine fills plastic bags with a mixture of beans,
broccoli, and other vegetables. Most of the bags contain the correct
weight, but because of the variation in the size of the beans and other
vegetables, a package might be underweight or overweight. A check of
4,000 packages filled in the past month revealed:
What is the probability that a particular package will be either
underweight or overweight?
P(A or C) = P(A) + P(C) = .025 + .075 = .10
LO4
5-13

Addition Rule – Not Mutually Exclusive Events
Example
What is the probability that a card chosen at
random from a standard deck of cards will be
either a king or a heart?
P(A or B) = P(A) + P(B) - P(A and B)
= 4/52 + 13/52 - 1/52
= 16/52, or .3077
LO4
5-14

The Complement Rule
The complement rule is used
to determine the probability of
an event occurring by
subtracting the probability of the
event not occurring from 1.
P(A) + P(~A) = 1
or P(A) = 1 - P(~A).
LO4
5-15

The Complement Rule - Example
An automatic Shaw machine fills plastic
bags with a mixture of beans, broccoli, and
other vegetables. Most of the bags contain
the correct weight, but because of the
variation in the size of the beans and other
vegetables, a package might be
underweight or overweight. Use the
complement rule to show the probability of
a satisfactory bag is .900
P(B) = 1 - P(~B)
= 1 – P(A or C)
= 1 – [P(A) + P(C)]
= 1 – [.025 + .075]
= 1 - .10
= .90
LO4
5-16

The General Rule of Addition
The Venn Diagram shows the result of a survey
of 200 tourists who visited Florida during the
year. The survey revealed that 120 went to
Disney World, 100 went to Busch Gardens and
60 visited both.
What is the probability a selected person visited
either Disney World or Busch Gardens?
P(Disney or Busch) = P(Disney) + P(Busch) - P(both Disney and Busch)
= 120/200 + 100/200 – 60/200
= .60 + .50 – .80
LO4
5-17

Joint Probability – Venn Diagram
JOINT PROBABILITY A probability that
measures the likelihood two or more events
will happen concurrently.
LO5 Define the term joint probability.
5-18

Special Rule of Multiplication
 The special rule of multiplication requires
that two events A and B are independent.
 Two events A and B are independent if the
occurrence of one has no effect on the
probability of the occurrence of the other.
 This rule is written: P(A and B) = P(A)P(B)
LO6 Calculate probabilities using the
rules of multiplication.
5-19

Multiplication Rule-Example
A survey by the American Automobile association (AAA) revealed 60
percent of its members made airline reservations last year. Two
members are selected at random. Since the number of AAA members
is very large, we can assume that R1 and R2 are independent.
What is the probability both made airline reservations last year?
Solution:
The probability the first member made an airline reservation last
year is .60, written as P(R1) = .60
The probability that the second member selected made a
reservation is also .60, so P(R2) = .60.
Since the number of AAA members is very large, it can be
assumes that R1 and R2 are independent.
P(R1 and R2) = P(R1)P(R2) = (.60)(.60) = .36
LO6
5-20

Conditional Probability
 A conditional probability is the
probability of a particular event occurring,
given that another event has occurred.
 The probability of the event A given that
the event B has occurred is written P(A|B).
LO7 Define the term conditional probability.
5-21

General Multiplication Rule
The general rule of multiplication is used to find the joint
probability that two independent events will occur.
It states that for two independent events, A and B, the joint
probability that both events will happen is found by
multiplying the probability that event A will happen by the
conditional probability of event B occurring given that A has
occurred.
LO7
5-22

General Multiplication Rule - Example
 The event that the first shirt selected is white is W1. The probability is P(W1) = 9/12
 The event that the second shirt (W2 )selected is also white. The conditional probability
that the second shirt selected is white, given that the first shirt selected is also white, is
P(W2 | W1) = 8/11.
 To determine the probability of 2 white shirts being selected we use formula:
P(AB) = P(A) P(B|A)
 P(W1 and W2) = P(W1)P(W2 |W1) = (9/12)(8/11) = 0.55
A golfer has 12 golf shirts in his closet.
Suppose 9 of these shirts are white and
the others blue. He gets dressed in the
dark, so he just grabs a shirt and puts it
on. He plays golf two days in a row and
does not do laundry.
What is the likelihood both shirts
selected are white?
LO7
5-23

Contingency Tables
A CONTINGENCY TABLE is a table used to classify sample
observations according to two or more identifiable
characteristics
E.g. A survey of 150 adults classified each as to gender and the
number of movies attended last month. Each respondent is
classified according to two criteria—the number of movies
attended and gender.
LO8 Calculate probabilities using a
contingency table.
5-24

Contingency Tables - Example
A sample of executives were surveyed about their loyalty to their
company. One of the questions was, “If you were given an offer by
another company equal to or slightly better than your present position,
would you remain with the company or take the other position?” The
responses of the 200 executives in the survey were cross-classified
with their length of service with the company.
What is the probability of randomly selecting an executive who is loyal
to the company (would remain) and who has more than 10 years of
service?
LO8
5-25

Contingency Tables - Example
Event A1 - if a randomly selected executive will remain with the
company despite an equal or slightly better offer from another
company. Since there are 120 executives out of the 200 in the
survey who would remain with the company
P(A1) = 120/200, or .60
Event B4 - if a randomly selected executive has more than 10
years of service with the company. Thus, P(B4| A1) is the
conditional probability that an executive with more than 10
years of service would remain with the company. Of the 120
executives who would remain 75 have more than 10 years of
service, so
P(B4| A1) = 75/120
LO8
5-26

Tree Diagrams
A tree diagram is:
1. useful for portraying conditional and joint probabilities.
2. particularly useful for analyzing business decisions
involving several stages.
3. a graph that is helpful in organizing calculations that
involve several stages.
Each segment in the tree is one stage of the problem.
The branches of a tree diagram are weighted by
probabilities.
LO8
5-27

Bayes’ Theorem
 Bayes’ Theorem is a method for revising a probability
given additional information.
 It is computed using the following formula:
LO9 Calculate probabilities
using Bayes’ Theorem
5-29

Bayes’ Theorem - Example
LO9
5-30

Bayes’ Theorem – Example
(cont.)
LO9
5-31

(cont.)
LO9
5-32

(cont.)
LO9
5-33

Counting Rules – Multiplication
The multiplication formula
indicates that if there are m ways of
doing one thing and n ways of
doing another thing, there are m x n
ways of doing both.
Example: Dr. Delong has 10 shirts
and 8 ties. How many shirt and tie
outfits does he have?
(10)(8) = 80
LO10 Determine the number of outcomes
using the appropriate principle of counting.
5-34

Counting Rules – Multiplication: Example
An automobile dealer wants
to advertise that for $29,999
you can buy a convertible, a
two-door sedan, or a four-
door model with your choice
of either wire wheel covers or
solid wheel covers. How
many different arrangements
of models and wheel covers
can the dealer offer?
LO10
5-35

Counting Rules - Permutation
A permutation is any arrangement of r
objects selected from n possible objects.
The order of arrangement is important in
permutations.
LO10
5-36

Counting - Combination
A combination is the number of
ways to choose r objects from a
group of n objects without regard to
order.
LO10
5-37

Combination and Permutation Examples
COMBINATION EXAMPLE
There are 12 players on the
Carolina Forest High School
basketball team. Coach
Thompson must pick five
players among the twelve on
the team to comprise the
starting lineup. How many
different groups are possible?
792
)!
5
12
(
!
5
!
12
5
12 


C
PERMUTATION EXAMPLE
Suppose that in addition to
selecting the group, he must
also rank each of the players
in that starting lineup
according to their ability.
040
,
95
)!
5
12
(
!
12
5
12 


P
LO10
5-38

Chap005Lind.ppt

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