Download Chapter 4 - El Camino College

Chapter 4
Probability
4-1 Review and Preview
4-2 Basic Concepts of Probability
4-3 Addition Rule
4-4 Multiplication Rule: Basics
4-5 Multiplication Rule: Complements and Conditional
Probability
4-6 Counting
4-7 Probabilities Through Simulations
4-8 Bayes' Theorem
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-1
Review
Necessity of sound sampling methods.
Common measures of characteristics of
data, such as the mean and the standard
deviation
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-2
Preview
Rare Event Rule for Inferential Statistics:
If, under a given assumption, the
probability of a particular observed event is
extremely small, we conclude that the
assumption is probably not correct.
Statisticians use the rare event rule for
inferential statistics.
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-3
Chapter 4
Probability
4-1 Review and Preview
4-2 Basic Concepts of Probability
4-3 Addition Rule
4-4 Multiplication Rule: Basics
4-5 Multiplication Rule: Complements and Conditional
Probability
4-6 Counting
4-7 Probabilities Through Simulations
4-8 Bayes’ Theorem
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-4
Key Concept
Three approaches to finding the probability
of an event.
The most important objective is to learn
how to interpret probability values.
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-5
Definitions

Event
any collection of results or outcomes of a procedure

Simple Event
an event that cannot be further broken down into
simpler components

Sample Space
for a procedure consists of all possible simple events
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-6
Example
“b” for baby boy and “g” for a baby girl.
Procedure
Example of
Event
Sample Space
Single birth
1 girl (simple
event)
{b, g}
3 births
2 boys and 1 girl
(bbg, bgb, and
gbb are all simple
events)
{bbb, bbg, bgb,
bgg, gbb, gbg,
ggb, ggg}
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-7
Notation for
Probabilities
P – a probability.
A – a event.
P(A) - the probability of event A occurring.
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Section 4.1-8
Basic Rules for
Computing Probability
Rule 1: Relative Frequency Approximation of
Probability
P(A) =
# of times A occurred
# of times procedure was repeated
Example: flip a coin. Flip a coin 10 times, let A = head,
# heads in 10 trials
P (head) 
10
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-9
Basic Rules for
Computing Probability
Rule 2: Classical Approach to Probability
(Requires Equally Likely Outcomes)
s
number of ways A can occur
P ( A) = =
n number of different simple events
Example. Roll a die. Let event A = odd number.
3
P (odd number ) 
6
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-10
Basic Rules for
Computing Probability
Rule 3: Subjective Probabilities
P(A), the probability of event A, is estimated by
using knowledge of the relevant circumstances.
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-11
Example
When three children are born, the sample space is:
{bbb, bbg, bgb, bgg, gbb, gbg, ggb, ggg}
Assuming that boys and girls are equally likely, find
the probability of getting three children of all the
same gender.
2
P  three children of the same gender    0.25
8
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-12
Probability Limits
 The probability of an impossible event is 0.
 The probability of an event that is certain to
occur is 1.
 For any event A,
0  P(A)  1.
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Section 4.1-13
Possible Values
for Probabilities
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Section 4.1-14
Complementary Events
The complement of event A, denoted by
A, consists of all outcomes in which the
event A does not occur.
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Section 4.1-15
Example
1010 United States adults were surveyed and 202
of them were smokers.
It follows that:
202
P  smoker  
 0.200
1010
202
P  not a smoker   1 
 0.800
1010
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-16
Rounding Off Probabilities
When expressing the value of a probability,
• either give the exact fraction
• Or decimal or round off to three significant digits.
• Suggestion: When a probability is not a simple
fraction such as 2/3 or 5/9, express it as a
decimal
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-17
Definition
An event is unlikely if its probability is very small,
such as 0.05 or less.
An event has an unusually low number of
outcomes of a particular type or an unusually high
number of those outcomes if that number is far
from what we typically expect.
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-18
Odds
The actual odds against event A occurring = P ( A) / P ( A) .
expressed in fractions in lowest form.
The actual odds in favor of event A occurring = P ( A) / P ( A)
expressed in fractions in lowest form.
payoff odds against event A = (net profit) : (amount bet)
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-19
Example
If you bet $5 on the number 13 in roulette, your
probability of winning is 1/38 and the payoff odds
are given by the casino at 35:1.
a.Find the actual odds against the outcome of 13.
37:1
a.How much net profit would you make if you win
by betting on 13?
$175
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-20
Chapter 4
Probability
4-1 Review and Preview
4-2 Basic Concepts of Probability
4-3 Addition Rule
4-4 Multiplication Rule: Basics
4-5 Multiplication Rule: Complements and Conditional
Probability
4-6 Counting
4-7 Probabilities Through Simulations
4-8 Bayes’ Theorem
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-21
Key Concept
• Consider P(A or B)
• The key word in this section is “or.” It is the
inclusive or, meaning, either A occurs, or B
occurs, or both A and B occurs.
• Using addition rule
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-22
Compound Event
Compound Event
any event combining 2 or more simple events
Notation
P(A or B) = P(in a single trial, event A occurs or
event B occurs or they both occur)
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Section 4.1-23
Compound Event
Formal Addition Rule
P(A or B) = P(A) + P(B) – P(A and B)
where P(A and B) denotes the probability that A and
B both occur at the same time.
Example. 74% speak English, 58% speak Spanish,
43% speak both English and Spanish. How many
speak English or Spanish?
89%
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-24
Example
Table 4-1 Pre-employment Drug Screening Results
Positive test result
(drug use is indicated)
Negative test results
(drug use is not indicated)
Subject uses drugs
44
(True positive)
6
(False negative)
Subject is not a drug user
90
(False positive)
860
(True negative)
Refer to the above table. If a subject is
randomly selected from the 1000 subjects, find
the probability of selecting a subject who had a
positive test result or use drugs.
P(positive or use drugs) = 0.140
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-25
Disjoint or Mutually Exclusive
Events A and B are disjoint (or mutually
exclusive) if they cannot occur at the same
time.
Venn Diagram for Events That Are
Not Disjoint
Venn Diagram for Disjoint Events
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Section 4.1-26
Disjoint or Mutually Exclusive
If A and B are disjoint, then
P(A or B) = P(A) + P(B).
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Section 4.1-27
Disjoint or Mutually Exclusive: Examples
Decide if two events are disjoint.
Example i.
a. randomly selecting someone who is a registered Democrat
b. randomly selecting someone who is a registered Republican
Disjoint
Example ii.
a. randomly selecting someone taking a statistics course
b. randomly selecting someone who is a female
Not disjoint
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-28
Complementary Events
A and A must be disjoint.
It is impossible for an event and its
complement to occur at the same time.
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Section 4.1-29
Rule of
Complementary Events
P( A)  P ( A)  1
P( A)  1  P( A)
P( A)  1  P( A)
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Section 4.1-30
Venn Diagram for the
Complement of Event A
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Section 4.1-31
Complement of Event A:
Example
Devilish belief. Based on data from a Harris
poll, the probability of randomly selecting
someone who believes in devil is 0.6.
If a person is randomly selected, find the
probability of getting someone who does not
believe in devil.
P(does not believe in devil) = 0.4
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-32
Chapter 4
Probability
4-1 Review and Preview
4-2 Basic Concepts of Probability
4-3 Addition Rule
4-4 Multiplication Rule: Basics
4-5 Multiplication Rule: Complements and Conditional
Probability
4-6 Counting
4-7 Probabilities Through Simulations
4-8 Bayes Theorem
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-33
Key Concept
Consider P(A and B)
The key word is “and”
Using multiplication rule.
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Section 4.1-34
Notation
P(A and B) = P(event A occurs in a first trial and
event B occurs in a second trial)
P(B | A) = P(event B occurring after event A has
already occurred)
Read as “probability of B given A”
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-35
Formal Multiplication Rule

P( A and B)  P( A)  P ( B | A)
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-36
Dependent and Independent
Two events A and B are independent if the
occurrence of one does not affect the probability
of the occurrence of the other.
If A and B are independent, then
P(A and B) = P(A)  P(B)
If A and B are not independent, they are said to
be dependent.
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-37
Dependent and Independent
Similarly, several events are independent if the
occurrence of any does not affect the probabilities
of the occurrence of the others.
If A1, A2, …, An are independent, then
P(A1 and A2 and … and An) = P(A1)P(A2)P(An)
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-38
Dependent and Independent
Important examples:
•Sampling with replacement: selections are
independent
•Sampling without replacement: selections are
dependent
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-39
Treating Dependent Events as
Independent
When small samples are drawn from large
populations. Treat the selection as independent.
In such cases, it is rare to select the same item
twice.
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-40
The 5% Guideline for
Cumbersome Calculations
If a sample size is no more than 5% of the size
of the population, treat the selections as being
independent.
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-41
Example
Suppose 50 drug test results are given from
people who use drugs:
Positive Test Results:
44
Negative Test Results:
6
Total Results:
50
If 2 of the 50 subjects are randomly selected find
the probability that the first person tested positive
and the second person tested negative:
44 6
a. without replacement, 50  49  0.108
b. with replacement,
44 6

 0.106
50 50
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-42
Example
When two different people are randomly selected
from those in your class, find the indicated
probability by assuming birthdays occur on the
same day of the week with equal frequencies.
a. Probability that two people are born on the
1
same day of the week.
7
b. Probability that two people are both born on
Monday.
1 1
1
 
.
7 7
49
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-43
Tree Diagrams
A tree diagram is a picture of the possible
outcomes of a procedure.
• Helpful in determining the number of
possible outcomes in a sample space,
• Good if the number of possibilities is
not too large.
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-44
Tree Diagrams
This figure
summarizes
the possible
outcomes
for a true/false
question followed
by a multiple choice
question.
Note that there are
10 possible
combinations.
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-45
Summary of Fundamentals
 In the addition rule, the word “or” in P(A or B)
suggests addition. Add P(A) and P(B), be
careful to add in such a way that every outcome
is counted only once.
 In the multiplication rule, the word “and” in P(A
and B) suggests multiplication. Multiply P(A) and
P(B), but be sure that the probability of event B
takes into account the previous occurrence of
event A.
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Section 4.1-46
Applying the
Multiplication Rule
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Section 4.1-47
Chapter 4
Probability
4-1 Review and Preview
4-2 Basic Concepts of Probability
4-3 Addition Rule
4-4 Multiplication Rule: Basics
4-5 Multiplication Rule: Complements and
Conditional Probability
4-6 Counting
4-7 Probabilities Through Simulations
4-8 Bayes’ Theorem
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-48
Key Concepts
Probability of “at least one”:
Find the probability that among several trials, we
get at least one of some specified event.
Conditional probability:
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Section 4.1-49
Complements: The Probability
of “At Least One”
 “At least one” is equivalent to “one or more.”
 The complement of getting at least one item of a
particular type is that you get no items of that
type.
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-50
Finding the Probability
of “At Least One”
To find the probability of at least one of
something, calculate the probability of
none and then subtract that result from 1.
That is,
P(at least one) = 1 – P(none).
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-51
Example
Topford supplies X-Data DVDs in lots of 50, and
they have a reported defect rate of 0.5% so the
probability of a disk being defective is 0.005. It
follows that the probability of a disk being good is
0.995.
What is the probability of getting at least one
defective disk in a lot of 50?
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-52
Example – continued
What is the probability of getting at least one
defective disk in a lot of 50?
P  at least 1 defective disk in 50  
1  P  all 50 disks are good  
1   0.995  
50
1  0.778  0.222
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-53
Conditional Probability
Conditional probability P ( B | A) denotes the
conditional probability of event B occurring,
given that event A has already occurred. It is
computed by
P( A and B)
P( B | A) 
P( A)
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-54
Example
Refer to the table to find the probability that a
subject actually uses drugs, given that he or she
had a positive test result.
Positive Drug Test
Negative Drug Test
Subject Uses Drugs
44 (True Positive)
6 (False Negative)
Subject Does
Not Use Drugs
90 (False Positive)
860 (True Negative)
P(subject uses drugs | subject had a positive test result)  0.328
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-55
Example - continued
Positive Drug Test
Negative Drug Test
Subject Uses Drugs
44 (True Positive)
6 (False Negative)
Subject Does
Not Use Drugs
90 (False Positive)
860 (True Negative)
P  subject uses drugs | subject tests positive  
P  subject uses drugs and subject tests positive 

P  subject tests positive 
44
1000  44  0.328
134
134
1000
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-56
Confusion of the Inverse
To incorrectly believe that P( A | B ) and
P ( B | A) are the same, or to incorrectly use
one value for the other, is often called
confusion of the inverse.
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-57
Chapter 4
Probability
4-1 Review and Preview
4-2 Basic Concepts of Probability
4-3 Addition Rule
4-4 Multiplication Rule: Basics
4-5 Multiplication Rule: Complements and Conditional
Probability
4-6 Counting
4-7 Probabilities Through Simulations
4-8 Bayes’ Theorem
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-58
Key Concept
In many probability problems, the big obstacle is
finding the total number of outcomes, and this
section presents several methods for finding such
numbers without directly listing and counting the
possibilities.
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-59
Fundamental Counting Rule
For a sequence of two events in which the
first event can occur m ways and the
second event can occur n ways, the events
together can occur a total of mn ways.
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Section 4.1-60
Notation
The factorial symbol ! denotes the product of
decreasing positive whole numbers.
For example,
4!  4  3  2 1  24
By special definition, 0! = 1.
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Section 4.1-61
Factorial Rule
Number of different permutations (order
counts) of n different items can be arranged
when all n of them are selected. (This
factorial rule reflects the fact that the first
item may be selected in n different ways,
the second item may be selected in n – 1
ways, and so on.)
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Section 4.1-62
Permutations Rule
(when items are all different)
Requirements:
1. There are n different items available. (This rule does not apply if
some of the items are identical to others.)
2. We select r of the n items (without replacement).
3. We consider rearrangements of the same items to be different
sequences. (The permutation of ABC is different from CBA and
is counted separately.)
If the preceding requirements are satisfied, the number of
permutations (or sequences) of r items selected from n available
items (without replacement) is
n!
n Pr 
(n  r )!
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Section 4.1-63
Permutations Rule
(when some items are identical to others)
Requirements:
1. There are n items available, and some items are identical to
others.
2. We select all of the n items (without replacement).
3. We consider rearrangements of distinct items to be different
sequences.
If the preceding requirements are satisfied, and if there are n1 alike, n2
alike, . . . nk alike, the number of permutations (or sequences) of all
items selected without replacement is
n!
n1 !n2 ! nk !
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-64
Combinations Rule
Requirements:
1. There are n different items available.
2. We select r of the n items (without replacement).
3. We consider rearrangements of the same items to be the same.
(The combination of ABC is the same as CBA.)
If the preceding requirements are satisfied, the number of
combinations of r items selected from n different items is
n!
n Cr 
(n  r )!r !
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-65
Permutations versus
Combinations
When different orderings of the same items
are to be counted separately, we have a
permutation problem, but when different
orderings are not to be counted separately,
we have a combination problem.
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-66
Example
A byte is a sequence of eight numbers, all either 0 or 1.
The number of possible bytes is
2  256 .
8
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Section 4.1-67
Example
A history pop quiz asks students to arrange the following
presidents in chronological order: Hayes, Taft, Polk,
Taylor, Grant, Pierce.
If an unprepared student totally guesses, what is the
probability of guessing correctly?
Possible arrangements: 6!  720
1
P  guessing correctly  
 0.00139
720
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Section 4.1-68
Example
In the Pennsylvania Match 6 Lotto, winning the jackpot
requires you select six different numbers from 1 to 49.
The winning numbers may be drawn in any order. Find
the probability of winning if one ticket is purchased.
n!
49!
Number of combinations: nCr 

 13,983,816
 n  r !r ! 43!6!
1
P  winning  
13,983,816
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Section 4.1-69