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Excursions in Modern Mathematics, 7e: 15.1 - 1 Copyright © 2010 Pearson Education, Inc.
15 Chances, Probabilities, and Odds
15.1 Random Experiments and Sample
Spaces
15.2 Counting Outcomes in Sample
Spaces
15.3 Permutations and Combinations
15.4 Probability Spaces
15.5 Equiprobable Spaces
15.6 Odds
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Excursions in Modern Mathematics, 7e: 15.1 - 2 Copyright © 2010 Pearson Education, Inc.
• Probability is the quantification of uncertainty.
• We will use the term random experiment to describe an activity or a process whose outcome cannot be predicted ahead of time.
• Examples of random experiments: tossing a coin, rolling a pair of dice, drawing cards out of a deck of cards, predicting the result of a football game, and forecasting the path of a hurricane.
Random Experiment
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Excursions in Modern Mathematics, 7e: 15.1 - 3 Copyright © 2010 Pearson Education, Inc.
• Associated with every random
experiment is the set of all of its possible
outcomes, called the sample space of
the experiment.
• For the sake of simplicity, we will
concentrate on experiments for which
there is only a finite set of outcomes,
although experiments with infinitely many
outcomes are both possible and
important.
Sample Space
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Excursions in Modern Mathematics, 7e: 15.1 - 4 Copyright © 2010 Pearson Education, Inc.
• We use the letter S to denote a sample
space and the letter N to denote the size
of the sample space S (i.e., the number
of outcomes in S).
Sample Space - Set Notation
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Excursions in Modern Mathematics, 7e: 15.1 - 5 Copyright © 2010 Pearson Education, Inc.
One simple random experiment is to toss a
quarter and observe whether it lands heads or
tails. The sample space can be described by
S = {H, T}, where H stands for Heads and T
for Tails. Here N = 2.
Example 15.1 Tossing a Coin
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Excursions in Modern Mathematics, 7e: 15.1 - 6 Copyright © 2010 Pearson Education, Inc.
Suppose we toss a coin twice and record the
outcome of each toss (H or T) in the order it
happens. What is the sample space?
Example 15.2 More Coin Tossing
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Excursions in Modern Mathematics, 7e: 15.1 - 7 Copyright © 2010 Pearson Education, Inc.
The sample space now is
S = {HH, HT, TH, TT}, where HT means that
the first toss came up H and the second toss
came up T, which is a different outcome from
TH (first toss T and second toss H). In this
sample space N = 4.
Example 15.2 More Coin Tossing
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Excursions in Modern Mathematics, 7e: 15.1 - 8 Copyright © 2010 Pearson Education, Inc.
Suppose now we toss two distinguishable
coins (say, a nickel and a quarter) at the
same time. What is the sample space?
Example 15.2 More Coin Tossing
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Excursions in Modern Mathematics, 7e: 15.1 - 9 Copyright © 2010 Pearson Education, Inc.
The sample space is still
S = {HH, HT, TH, TT}. (Here we must agree
what the order of the symbols is–for example,
the first symbol describes the quarter and the
second the nickel.)
Example 15.2 More Coin Tossing
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Excursions in Modern Mathematics, 7e: 15.1 - 10 Copyright © 2010 Pearson Education, Inc.
Since they have the same sample space, we
will consider the two previous random
experiments as the same random experiment.
Example 15.2 More Coin Tossing
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Excursions in Modern Mathematics, 7e: 15.1 - 11 Copyright © 2010 Pearson Education, Inc.
Suppose we toss a coin twice, but we only
care now about the number of heads that
come up. What is the sample space?
Example 15.2 More Coin Tossing
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Excursions in Modern Mathematics, 7e: 15.1 - 12 Copyright © 2010 Pearson Education, Inc.
Here there are only three possible outcomes
(no heads, one head, or both heads), and
symbolically we might describe this sample
space as
S = {0, 1, 2}.
Example 15.2 More Coin Tossing
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Excursions in Modern Mathematics, 7e: 15.1 - 13 Copyright © 2010 Pearson Education, Inc.
The experiment is to roll a pair of dice. What is the sample space?
Example 15.5 Dice Rolling
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Excursions in Modern Mathematics, 7e: 15.1 - 14 Copyright © 2010 Pearson Education, Inc.
Example 15.5 More Dice Rolling
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Excursions in Modern Mathematics, 7e: 15.1 - 15 Copyright © 2010 Pearson Education, Inc.
Here we have a sample space with 36 different outcomes. Notice that the dice are colored white and red, a symbolic way to emphasize the fact that we are treating the dice as distinguishable objects. That is why the following rolls are distinguishable.
Example 15.5 More Dice Rolling
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Excursions in Modern Mathematics, 7e: 15.1 - 16 Copyright © 2010 Pearson Education, Inc.
Example 15.5 More Dice Rolling The sample space has 36 possible outcomes:
{(1,1) (1,2) (1,3) (1,4) (1,5) (1,6)
(2,1) (2,2) (2,3) (2,4) (2,5) (2,6)
(3,1) (3,2) (3,3) (3,4) (3,5) (3,6)
(4,1) (4,2) (4,3) (4,4) (4,5) (4,6)
(5,1) (5,2) (5,3) (5,4) (5,5) (5,6)
(6,1) (6,2) (6,3) (6,4) (6,5) (6,6)}
where the pairs represent the numbers rolled on
each dice (white, red).
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Excursions in Modern Mathematics, 7e: 15.1 - 17 Copyright © 2010 Pearson Education, Inc.
Roll a pair of dice and consider the total of the
two numbers rolled. What is the sample
space?
Example 15.4 Rolling a Pair of Dice
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Excursions in Modern Mathematics, 7e: 15.1 - 18 Copyright © 2010 Pearson Education, Inc.
The possible outcomes in this scenario range
from “rolling a two” to “rolling a twelve,” and
the sample space can be described by
S = {2, 3, 4, 5, 6, 7, 8, 9, 10,11,12}.
Example 15.4 Rolling a Pair of Dice
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Excursions in Modern Mathematics, 7e: 15.1 - 19 Copyright © 2010 Pearson Education, Inc.
• Page 577, problem 3
Examples
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Excursions in Modern Mathematics, 7e: 15.1 - 20 Copyright © 2010 Pearson Education, Inc.
• Page 577, problem 3
Solution:
• {ABCD, ABDC, ACBD, ACDB, ADBC,
ADCB, BACD, BADC, BCAD, BCDA,
BDAC, BDCA, CABD, CADB, CBAD,
CBDA, CDAB, CDBA, DABC, DACB,
DBAC, DBCA, DCAB, DCBA}
• There are 24 outcomes.
Examples
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Excursions in Modern Mathematics, 7e: 15.1 - 21 Copyright © 2010 Pearson Education, Inc.
We would like to understand what the
sample space looks like without necessarily
writing all the outcomes down. Our real goal
is to find N, the size of the sample space. If
we can do it without having to list all the
outcomes, then so much the better.
Not Listing All of the Outcomes
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Excursions in Modern Mathematics, 7e: 15.1 - 22 Copyright © 2010 Pearson Education, Inc.
15 Chances, Probabilities, and Odds
15.1 Random Experiments and Sample
Spaces
15.2 Counting Outcomes in Sample
Spaces
15.3 Permutations and Combinations
15.4 Probability Spaces
15.5 Equiprobable Spaces
15.6 Odds
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Excursions in Modern Mathematics, 7e: 15.1 - 23 Copyright © 2010 Pearson Education, Inc.
• If we toss a coin three times and separately record the outcome of each toss, the sample space is given by S = {HHH, HHT, HTH, HTT, THH, THT, TTH, TTT}.
• Here we can just count the outcomes and get N = 8.
Example 15.7 Tossing More Coins
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Excursions in Modern Mathematics, 7e: 15.1 - 24 Copyright © 2010 Pearson Education, Inc.
• Toss a coin 10 times
• In this case the sample space S is too big to write down
• We can “count” the number of outcomes in S without having to tally them one by one using the multiplication rule.
Example 15.7 Tossing More Coins
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Excursions in Modern Mathematics, 7e: 15.1 - 25 Copyright © 2010 Pearson Education, Inc.
• Suppose an activity consists of a series of events in which there are a possible outcomes for the first event, b possible outcomes for the second event, c possible outcomes for the third event, and so on.
• Then the total number of different possible outcomes for the series of events is: a · b · c · …
Multiplication Rule
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Excursions in Modern Mathematics, 7e: 15.1 - 26 Copyright © 2010 Pearson Education, Inc.
• Toss a coin ten times. How many
outcomes are in the sample space?
• There are two outcomes on the first toss
• There are two outcomes on the second
toss, etc.
• The total number of possible outcomes is
found by multiplying ten two’s together.
Example 15.7 Tossing More Coins
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Excursions in Modern Mathematics, 7e: 15.1 - 27 Copyright © 2010 Pearson Education, Inc.
• Total number of outcomes if a coin is
tossed ten times:
• Thus N = 210 = 1024.
Example 15.7 Tossing More Coins
factors 10
2222N
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Excursions in Modern Mathematics, 7e: 15.1 - 28 Copyright © 2010 Pearson Education, Inc.
Dolores is a young saleswoman planning her
next business trip. She is thinking about
packing three different pairs of shoes, four
skirts, six blouses, and two jackets. How
many different outfits will she be able to
create by combining these items? (Assume
that an outfit consists of one pair of shoes,
one skirt, one blouse, and one jacket.)
Example 15.8 The Making of a
Wardrobe
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Excursions in Modern Mathematics, 7e: 15.1 - 29 Copyright © 2010 Pearson Education, Inc.
Let’s assume that an outfit consists of one
pair of shoes, one skirt, one blouse, and one
jacket. Then to make an outfit Dolores must
choose a pair of shoes (three choices), a skirt
(four choices), a blouse (six choices), and a
jacket (two choices). By the multiplication rule
the total number of possible outfits is
3 4 6 2 = 144.
Example 15.8 The Making of a
Wardrobe
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Excursions in Modern Mathematics, 7e: 15.1 - 30 Copyright © 2010 Pearson Education, Inc.
Five candidates are running in an election,
with the top three vote getters elected (in
order) as President, Vice President, and
Secretary. How many different ways are there
to choose the five candidates to fill these
three positions?
Example 15.10 Ranking the Candidate
in an Election: Part 2
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Excursions in Modern Mathematics, 7e: 15.1 - 31 Copyright © 2010 Pearson Education, Inc.
Example 15.10 Ranking the Candidate
in an Election: Part 2
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Excursions in Modern Mathematics, 7e: 15.1 - 32 Copyright © 2010 Pearson Education, Inc.
• Page 578, problem 14
Examples
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Excursions in Modern Mathematics, 7e: 15.1 - 33 Copyright © 2010 Pearson Education, Inc.
• Solution to part (a)
Examples
320,4012345678
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Excursions in Modern Mathematics, 7e: 15.1 - 34 Copyright © 2010 Pearson Education, Inc.
• Solution to part (a)
Examples
160,2012345674
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Excursions in Modern Mathematics, 7e: 15.1 - 35 Copyright © 2010 Pearson Education, Inc.
• Solution to part (c)
Examples
57611223344
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Excursions in Modern Mathematics, 7e: 15.1 - 36 Copyright © 2010 Pearson Education, Inc.
15 Chances, Probabilities, and Odds
15.1 Random Experiments and Sample
Spaces
15.2 Counting Outcomes in Sample
Spaces
15.3 Permutations and Combinations
(OMIT)
15.4 Probability Spaces
15.5 Equiprobable Spaces
15.6 Odds
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Excursions in Modern Mathematics, 7e: 15.1 - 37 Copyright © 2010 Pearson Education, Inc.
• An event is any subset of the sample
space. That is, an event is any set of
individual outcomes.
• This definition includes the possibility of an
“event” that has no outcomes as well as
events consisting of a single outcome.
• We denote events as E and the outcomes
that make up E are listed inside braces {
and } (that is, set notation)
Events
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Excursions in Modern Mathematics, 7e: 15.1 - 38 Copyright © 2010 Pearson Education, Inc.
• An event that consists of no outcomes is
an impossible event.
– For the impossible event we use the empty
set so that E={ }.
• An event that consists of just one outcome
is a simple event.
Events
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Excursions in Modern Mathematics, 7e: 15.1 - 39 Copyright © 2010 Pearson Education, Inc.
• Suppose we toss a coin three times. The
sample space for this experiment is:
S = {HHH, HHT, HTH, HTT, THH, THT, TTH, TTT }.
• Here N=8.
• Table 15-2 shows examples of events.
Example 15.16 Coin-Tossing Event
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Excursions in Modern Mathematics, 7e: 15.1 - 40 Copyright © 2010 Pearson Education, Inc.
Example 15.16 Coin-Tossing Event
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Excursions in Modern Mathematics, 7e: 15.1 - 41 Copyright © 2010 Pearson Education, Inc.
Recall the example 15.5.
The experiment is to roll a pair of dice, one red and one white. The sample space is depicted on the next page.
Dice Rolling
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Excursions in Modern Mathematics, 7e: 15.1 - 42 Copyright © 2010 Pearson Education, Inc.
Example 15.5 More Dice Rolling
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Excursions in Modern Mathematics, 7e: 15.1 - 43 Copyright © 2010 Pearson Education, Inc.
a) Write the event E of rolling a sum of 7.
b) Write the event E of rolling the same
numbers on both dice.
c) Write the event of rolling at least one even
number.
Events
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Excursions in Modern Mathematics, 7e: 15.1 - 44 Copyright © 2010 Pearson Education, Inc.
(a)
Or
E={(1,6), (2,5), (3,4), (4,3), (5,2), (6,1)}
Events
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Excursions in Modern Mathematics, 7e: 15.1 - 45 Copyright © 2010 Pearson Education, Inc.
(b)
E={(1,1), (2,2), (3,3), (4,4), (5,5), (6,6)}
Events
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Excursions in Modern Mathematics, 7e: 15.1 - 46 Copyright © 2010 Pearson Education, Inc.
(c)
E = {(1,2), (1,4), (1,6), (2,2), (2,4), (2,6),
(3,2), (3,4), (3,6), (4,2), (4,4), (4,6),
(5,2), (5,4), (5,6), (6,2), (6,4), (6,6),}
Events
![Page 47: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/47.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 47 Copyright © 2010 Pearson Education, Inc.
• Page 580, problem 44
Examples
![Page 48: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/48.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 48 Copyright © 2010 Pearson Education, Inc.
• Solution to part (a)
Examples
},,,,,{1 FFTTFTFTFTTFTFFTTFTFTTFFE
![Page 49: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/49.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 49 Copyright © 2010 Pearson Education, Inc.
• Solution to part (b)
Examples
},,,,
,,,,,,{2
TTTTFTTTTFTTTTFTTTTF
FFTTFTFTFTTFTFFTTFTFTTFFE
![Page 50: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/50.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 50 Copyright © 2010 Pearson Education, Inc.
• Solution to part (c)
Examples
},,,,
,,,,,,{3
FFFFTFFFFTFFFFTFFFFT
FFTTFTFTFTTFTFFTTFTFTTFFE
![Page 51: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/51.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 51 Copyright © 2010 Pearson Education, Inc.
• Solution to part (d)
Examples
},,,{4 TTTTTTTFTTFTTTFFE
![Page 52: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/52.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 52 Copyright © 2010 Pearson Education, Inc.
Probability
• Defined as the long-term proportion of times the outcome occurs
Building Blocks of Probability
• Experiment - any activity for which the
outcome is uncertain
• Outcome - the result of a single performance of an experiment
• Sample space (S) - collection of all possible outcomes
• Event - collection of outcomes from the sample space
![Page 53: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/53.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 53 Copyright © 2010 Pearson Education, Inc.
Probability
• The probability for any event E is
always between 0 and 1.
• If the event is impossible, its
probability is 0.
• If the event is equal to the sample
space, its probability is 1.
![Page 54: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/54.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 54 Copyright © 2010 Pearson Education, Inc.
• A probability assignment assigns to
each simple event E in the sample space
a number between 0 and 1, which
represents the probability of the event E
and which we denote by Pr(E).
• Pr({ })=0
• Pr(S)=1
Probability Assignment
![Page 55: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/55.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 55 Copyright © 2010 Pearson Education, Inc.
Every probability assignment must obey the Law of Total Probability:
For any experiment, the sum of all the outcome probabilities in the sample space must equal 1.
Probability Assignment
![Page 56: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/56.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 56 Copyright © 2010 Pearson Education, Inc.
There are six players playing in a tennis
tournament: A (Russian, female), B (Croatian,
male), C (Australian, male), D (Swiss, male),
E (American, female), and F (American,
female). To handicap the winner of the
tournament we need a probability assignment
on the sample space S = {A, B, C, D, E, F }.
Example 15.18 Handicapping a
Tennis Tournament
![Page 57: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/57.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 57 Copyright © 2010 Pearson Education, Inc.
With sporting events the probability
assignment is subjective (it reflects an
opinion), but a professional odds-maker
comes up with the following probability
assignment:
Example 15.18 Handicapping a
Tennis Tournament
Event A B C D E F
Probability 0.08 0.16 0.20 0.25 0.16 0.15
![Page 58: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/58.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 58 Copyright © 2010 Pearson Education, Inc.
• Each probability is between 0 and 1.
• The sum of all outcome probabilities is 1
Example 15.18 Handicapping a
Tennis Tournament
1
15.016.025.020.016.008.0
)Pr()Pr()Pr()Pr()Pr()Pr( FEDCBA
![Page 59: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/59.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 59 Copyright © 2010 Pearson Education, Inc.
Once a specific probability assignment is
made on a sample space, the combination of
the sample space and the probability
assignment is called a probability space.
Example:
Probability Space
Event A B C D E F
Probability 0.08 0.16 0.20 0.25 0.16 0.15
S = {A, B, C, D, E, F }
![Page 60: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/60.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 60 Copyright © 2010 Pearson Education, Inc.
• Page 580, problem 38
Examples
![Page 61: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/61.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 61 Copyright © 2010 Pearson Education, Inc.
• Solution part (a)
Examples
Pr(o1)=0.15= Pr(o4)
Pr(o2)=0.35= Pr(o3)
![Page 62: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/62.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 62 Copyright © 2010 Pearson Education, Inc.
• Solution part (b)
Examples
Pr(o1)=0.15
Pr(o2)=0.35
Pr(o3)=0.22
Pr(o4)=0.28
![Page 63: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/63.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 63 Copyright © 2010 Pearson Education, Inc.
15 Chances, Probabilities, and Odds
15.1 Random Experiments and Sample
Spaces
15.2 Counting Outcomes in Sample
Spaces
15.3 Permutations and Combinations
15.4 Probability Spaces
15.5 Equiprobable Spaces
15.6 Odds
![Page 64: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/64.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 64 Copyright © 2010 Pearson Education, Inc.
• Events that have no outcomes in common
are called mutually exclusive events.
Mutually Exclusive Events
![Page 65: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/65.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 65 Copyright © 2010 Pearson Education, Inc.
• Example of mutually exclusive events:
dicecolor different on two 7 of sum a rolling ofevent 1E
dicecolor different on twonumber same therolling ofevent 2E
)}1,6(),2,5(),3,4(),4,3(),5,2(),6,1{(1E
)}6,6(),5,5(),4,4(),3,3(),2,2(),1,1{(2E
Mutually Exclusive Events
![Page 66: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/66.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 66 Copyright © 2010 Pearson Education, Inc.
• Example of events which are not mutually
exclusive:
dicecolor different on two 7 of sum a rolling ofevent 1E
dicecolor different on two 6 oneleast at rolling ofevent 2E
)}1,6(),2,5(),3,4(),4,3(),5,2(),6,1{(1E
}(6,5)(6,4),(6,3),(6,2),(6,1),
),6,6(),6,5(),6,4(),6,3(),6,2(),6,1{(2E
Mutually Exclusive Events
![Page 67: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/67.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 67 Copyright © 2010 Pearson Education, Inc.
• If events A and B are mutually exclusive,
then
Pr(A or B) = Pr(A)+Pr(B)
• If events A, B, and C are mutually
exclusive, then
Pr(A or B or C) = Pr(A)+Pr(B)+Pr(C)
Etc.
Mutually Exclusive Events
![Page 68: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/68.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 68 Copyright © 2010 Pearson Education, Inc.
(from 15.4) There are six players playing in a
tennis tournament and the events in the
sample space are:
A (Russian, female),
B (Croatian, male),
C (Australian, male),
D (Swiss, male),
E (American, female), and
F (American, female)
Example 15.18 Handicapping a
Tennis Tournament
![Page 69: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/69.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 69 Copyright © 2010 Pearson Education, Inc.
The probability space for the tournament
winner is:
Example 15.18 Handicapping a
Tennis Tournament
Event A B C D E F
Probability 0.08 0.16 0.20 0.25 0.16 0.15
S = {A, B, C, D, E, F }
Use the probability space to find the probability that an
American will win the tournament.
![Page 70: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/70.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 70 Copyright © 2010 Pearson Education, Inc.
Pr(E or F) = Pr(E) + Pr(F)
= 0.16 + 0.15
= 0.31
Example 15.18 Handicapping a
Tennis Tournament
![Page 71: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/71.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 71 Copyright © 2010 Pearson Education, Inc.
What is the probability that a male will win the
tournament?
Example 15.18 Handicapping a
Tennis Tournament
![Page 72: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/72.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 72 Copyright © 2010 Pearson Education, Inc.
Pr(B or C or D)
=Pr(B) + Pr(C) + Pr(D)
= 0.16 + 0.20 + 0.25
= 0.61
Example 15.18 Handicapping a
Tennis Tournament
![Page 73: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/73.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 73 Copyright © 2010 Pearson Education, Inc.
What is the probability that an American male
will win the tournament?
Example 15.18 Handicapping a
Tennis Tournament
![Page 74: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/74.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 74 Copyright © 2010 Pearson Education, Inc.
Pr({ }) = 0
(since this one is an impossible event–there
are no American males in the tournament)
Example 15.18 Handicapping a
Tennis Tournament
![Page 75: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/75.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 75 Copyright © 2010 Pearson Education, Inc.
• Next we determine how to calculate
probabilities for random experiments.
Calculating Probabilities
![Page 76: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/76.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 76 Copyright © 2010 Pearson Education, Inc.
• What does honesty mean when applied
to coins, dice, or decks of cards?
• It essentially means that all individual
outcomes in the sample space are
equally probable.
• Thus, an honest coin is one in which H
and T have the same probability of
coming up, and an honest die is one in
which each of the numbers 1 through 6 is
equally likely to be rolled.
What Does Honesty Mean?
![Page 77: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/77.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 77 Copyright © 2010 Pearson Education, Inc.
• A probability space in which each simple
event has an equal probability of
occurring is called an equiprobable
space.
• Not all probability spaces are
equiprobable as the next example
shows.
Equiprobable Spaces
![Page 78: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/78.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 78 Copyright © 2010 Pearson Education, Inc.
Suppose that you are playing a game that
involves rolling a pair of honest dice, and the
only thing that matters is the total of the two
numbers rolled. The sample space in this
situation is
S = {2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12},
where the outcomes are the possible totals
that could be rolled.
Example 15.20 Rolling a Pair of Honest
Dice
![Page 79: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/79.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 79 Copyright © 2010 Pearson Education, Inc.
This sample space would not represent an
equiprobable space. For example, the
likelihood of rolling a 7 is much higher than
that of rolling a 12.
Example 15.20 Rolling a Pair of Honest
Dice
![Page 80: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/80.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 80 Copyright © 2010 Pearson Education, Inc.
• If we know the probability space is an
equiprobable space, we can determine
the probability of any outcome as follows.
Equiprobable Spaces
![Page 81: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/81.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 81 Copyright © 2010 Pearson Education, Inc.
If k denotes the size of an event E and N
denotes the size of the sample space S,
then in an equiprobable space
PROBABILITIES IN
EQUIPROBABLE SPACES
Pr E
k
N
![Page 82: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/82.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 82 Copyright © 2010 Pearson Education, Inc.
Suppose that a coin is tossed three times,
and we have been assured that the coin is an
honest coin. If this is true, then each of the
eight possible outcomes in the sample space:
{HHH HHT HTH THH TTH THT HTT TTT}
has probability 1/8.
Example 15.19 Honest Coin Tossing
![Page 83: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/83.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 83 Copyright © 2010 Pearson Education, Inc.
This table shows each of the events with
their respective probabilities.
Example 15.19 Honest Coin Tossing
![Page 84: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/84.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 84 Copyright © 2010 Pearson Education, Inc.
Example 15.20 More Dice Rolling If we roll a pair of honest dice (one red and one white), each
of the 36 outcomes is equally likely:
S = {(1,1) (1,2) (1,3) (1,4) (1,5) (1,6)
(2,1) (2,2) (2,3) (2,4) (2,5) (2,6)
(3,1) (3,2) (3,3) (3,4) (3,5) (3,6)
(4,1) (4,2) (4,3) (4,4) (4,5) (4,6)
(5,1) (5,2) (5,3) (5,4) (5,5) (5,6)
(6,1) (6,2) (6,3) (6,4) (6,5) (6,6)}
where the pairs represent the numbers rolled on
each dice (white, red).
![Page 85: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/85.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 85 Copyright © 2010 Pearson Education, Inc.
Because the dice are honest, each of these
36 possible outcomes is equally likely to
occur, so the probability of each is 1/36.
Example 15.20 Rolling a Pair of Honest
Dice
![Page 86: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/86.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 86 Copyright © 2010 Pearson Education, Inc.
Table 15-5 (next slide) shows the probability
of rolling a sum of 2, 3, 4, . . . , 12.
Example 15.20 Rolling a Pair of Honest
Dice
![Page 87: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/87.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 87 Copyright © 2010 Pearson Education, Inc.
![Page 88: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/88.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 88 Copyright © 2010 Pearson Education, Inc.
• Page 581, problem 50 (a) and (c)
Examples
![Page 89: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/89.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 89 Copyright © 2010 Pearson Education, Inc.
There are 16 outcomes in this sample space:
Example 15.22 Rolling a Pair of Honest
Dice
1622222 4N
![Page 90: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/90.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 90 Copyright © 2010 Pearson Education, Inc.
• Solution to part (a)
which has k=6 outcomes so that
Examples
},,,,,{1 FFTTFTFTFTTFTFFTTFTFTTFFE
375.08
3
16
6)Pr( 1E
![Page 91: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/91.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 91 Copyright © 2010 Pearson Education, Inc.
• Solution to part (c)
which has k=11 outcomes so that
Examples
},,,,
,,,,,,{3
FFFFTFFFFTFFFFTFFFFT
FFTTFTFTFTTFTFFTTFTFTTFFE
6875.016
11)Pr( 3E
![Page 92: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/92.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 92 Copyright © 2010 Pearson Education, Inc.
Example
Find the probability of drawing an ace when
drawing a single card at random from a
standard deck of 52 cards.
![Page 93: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/93.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 93 Copyright © 2010 Pearson Education, Inc.
Example continued
Solution
• The sample space for the experiment:
![Page 94: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/94.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 94 Copyright © 2010 Pearson Education, Inc.
Example continued
• If a card is chosen at random, then each card has the same chance of being drawn.
• There are 52 outcomes in this sample space, so N = 52.
![Page 95: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/95.jpg)
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Example 5.1 continued
• Let E be the event that an ace is drawn.
• Event E consists of the four aces
{A♥, A♦, A♣, A♠}, so k = 4.
• Therefore, the probability of drawing
an ace is
Pr(E) = 4/52 = 1/13 = 0.0769
![Page 96: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/96.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 96 Copyright © 2010 Pearson Education, Inc.
Experiment: A pair of dice are rolled. Define the
following events
Find the probability that the sum of the two dice
equals five or the sum of the two dice equals 2.
5 equals dice two theof sum event the A
2 is dice two theof sum event the B
Example
![Page 97: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/97.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 97 Copyright © 2010 Pearson Education, Inc.
Example
The event that the sum of the two dice equals five
or the event that the sum of the two dice equals two
are mutually exclusive and
)Pr()Pr()or Pr( BABA
![Page 98: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/98.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 98 Copyright © 2010 Pearson Education, Inc.
Example
The event that the sum of the two dice equals five.
1,1 1,2 1,3 1,4 1,5 1,6
2,1 2,2 2,3 2,4 2,5 2,6
3,1 3,2 3,3 3,4 3,5 3,6
4,1 4,2 4,3 4,4 4,5 4,6
5,1 5,2 5,3 5,4 5,5 5,6
6,1 6,2 6,3 6,4 6,5 6,6
)}1,4(),2,3(),3,2(),4,1{(A
![Page 99: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/99.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 99 Copyright © 2010 Pearson Education, Inc.
Example
The probability that the sum of the two dice equals five.
111.09
1
36
4)Pr(
N
kA
![Page 100: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/100.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 100 Copyright © 2010 Pearson Education, Inc.
Example
The event that the sum of the two dice equals two.
1,1 1,2 1,3 1,4 1,5 1,6
2,1 2,2 2,3 2,4 2,5 2,6
3,1 3,2 3,3 3,4 3,5 3,6
4,1 4,2 4,3 4,4 4,5 4,6
5,1 5,2 5,3 5,4 5,5 5,6
6,1 6,2 6,3 6,4 6,5 6,6
)}1,1{(B
![Page 101: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/101.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 101 Copyright © 2010 Pearson Education, Inc.
Example
The probability that the sum of the two dice equals two.
0278.036
1)Pr(
N
kB
![Page 102: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/102.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 102 Copyright © 2010 Pearson Education, Inc.
Example
)Pr()Pr()or Pr( BABA
139.036
5
36
1
36
4
![Page 103: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/103.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 103 Copyright © 2010 Pearson Education, Inc.
• Page 581, problem 55 (d)
Example
![Page 104: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/104.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 104 Copyright © 2010 Pearson Education, Inc.
• The outcomes in the sample space consist
of a sequence 10 of correct and incorrect
guesses (possibly all correct and possibly
all incorrect.
• For example, if we denote the event of
getting an answer correct as C and
incorrect as I we have outcomes like:
{ICICICICCI, CIIICCICCC, ETC.}
Example
![Page 105: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/105.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 105 Copyright © 2010 Pearson Education, Inc.
• Each sequence has an associated score:
{ICICICICCI, CIIICCICCC, ETC.}
Example
5.25.25score 426score
![Page 106: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/106.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 106 Copyright © 2010 Pearson Education, Inc.
There are 1024 outcomes in this sample space:
1024210N
Example
![Page 107: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/107.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 107 Copyright © 2010 Pearson Education, Inc.
• To get 8 or more points you can get all 10
correct (score=10 points) or 9 correct and
1 incorrect (score=8.5 points)
Denote:
A = event get all ten correct
B = event you get 9 correct and 1 incorrect
Example
![Page 108: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/108.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 108 Copyright © 2010 Pearson Education, Inc.
There is one outcome in the sample space that corresponds to getting all ten correct:
A={CCCCCCCCCC}
1024
1)Pr(A
Example
![Page 109: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/109.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 109 Copyright © 2010 Pearson Education, Inc.
• There are 10 outcomes in the sample
space to getting 9 correct (C) and 1
incorrect (I):
B={CCCCCCCCCI, CCCCCCCCIC, ETC.}
Example
1024
10)Pr(B
![Page 110: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/110.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 110 Copyright © 2010 Pearson Education, Inc.
Example 15.22 Rolling a Pair of Honest
Dice
0107.01024
11
1024
10
1024
1
)Pr()Pr()or Pr( BABA
Since events A and B are mutually exclusive we get that
![Page 111: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/111.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 111 Copyright © 2010 Pearson Education, Inc.
Complement of E
• The complement of an event E is denoted:
EC
• Collection of outcomes in the sample space that are not in event E
• Complement comes from the word “to complete”
• Any event and its complement together make up the complete sample space
![Page 112: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/112.jpg)
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Example
If a fair coin is tossed three times.
E = event that exactly one heads occurs (for example, HTT)
What is the complement of E = EC ?
![Page 113: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/113.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 113 Copyright © 2010 Pearson Education, Inc.
Example
Sample space:
Event E:
Complement of E:
},,,,,,,{ TTTTTHTHTTHHHTTHTHHHTHHHS
},,,,{ TTTTHHHTHHHTHHHEC
},,{ TTHTHTHTTE
![Page 114: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/114.jpg)
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Example: find the probability of the complement of an event
E is the event “observing a sum of 4 when
the two fair (honest) dice are rolled,”
Find the probability that you do not roll a 4.
Pr(EC)
![Page 115: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/115.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 115 Copyright © 2010 Pearson Education, Inc.
Example continued
Solution
• Which outcomes belong to EC?
• There are the following outcomes in E:
{(3,1)(2,2)(1,3)}.
![Page 116: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/116.jpg)
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Example continued
• All the outcomes except the outcomes from A in the two-dice sample space:
![Page 117: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/117.jpg)
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Example continued
• There are 33 outcomes in EC and 36 outcomes in the sample space.
• This gives the probability of not rolling a 4 to be
• The probability is high that, on this roll at least, your roommate will not land on Boardwalk.
Pr(EC) = 33/36 = 11/12 = 0.917
![Page 118: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/118.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 118 Copyright © 2010 Pearson Education, Inc.
Probabilities for Complements
• For any event E and its complement EC,
Pr(E) + Pr(EC) = 1
OR: Pr(E) = 1 - Pr(EC)
OR: Pr(EC ) = 1 - Pr(E)
![Page 119: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/119.jpg)
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Example
Consider the experiment of drawing a
card at random from a shuffled deck of 52 cards. Find the probability of drawing a card that is not a face card.
NOTE: a face card is a king, queen, or jack
![Page 120: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/120.jpg)
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• The sample space for the experiment where a subject chooses a single card at random from a deck of cards is depicted here.
Example
![Page 121: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/121.jpg)
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Example
ANSWER:
There are 52 cards, 12 of which are face
cards.
769.0231.01)Pr( CE
card face a drawyou event E
card face a drawnot doyou event CE
231.052
12)Pr(
N
kE
![Page 122: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/122.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 122 Copyright © 2010 Pearson Education, Inc.
There are six players playing in a tennis
tournament and the events in the sample
space are:
A (Russian, female),
B (Croatian, male),
C (Australian, male),
D (Swiss, male),
E (American, female), and
F (American, female)
Example Handicapping a
Tennis Tournament
![Page 123: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/123.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 123 Copyright © 2010 Pearson Education, Inc.
The probability space for the tournament
winner is:
Example Handicapping a
Tennis Tournament
Event A B C D E F
Probability 0.08 0.16 0.20 0.25 0.16 0.15
S = {A, B, C, D, E, F }
Use the probability space to find the probability that a
Russian will not win the tournament.
![Page 124: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/124.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 124 Copyright © 2010 Pearson Education, Inc.
The probability a Russian will win the
tournament is:
The probability a Russian will not win the
tournament is:
Example Handicapping a
Tennis Tournament
08.0)Pr(A
92.008.01)Pr( CA
![Page 125: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/125.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 125 Copyright © 2010 Pearson Education, Inc.
Two events are said to be independent
events if the occurrence of one event does
not affect the probability of the occurrence of
the other.
Independent Events
![Page 126: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/126.jpg)
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Examples of independent events:
1.Flip a coin three times; the outcome of any
one flip does not affect the probability of
another flip.
2.Roll a die four times; the outcome of any
one roll does not affect the probability of
another roll.
Independent Events
![Page 127: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/127.jpg)
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Imagine a game in which you roll an honest die four times. Find the probability that you roll a number that is not one on all four rolls of the dice.
Example 15.22 Rolling a Pair of Honest
Dice
![Page 128: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/128.jpg)
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There are 1296 outcomes in this sample space:
k = number of events corresponding to the event that you roll a number that is not one on all four rolls of the dice
Example 15.22 Rolling a Pair of Honest
Dice
129666666 4N
![Page 129: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/129.jpg)
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Let R1, R2, R3, and R4 denote the outcomes on roll 1, roll 2, roll 3, and roll 4.
Examples of outcomes that you do not roll 1:
R1 = 5, R2 = 3, R3 = 4, R4 = 2
R1 = 4, R2 = 6, R3 = 2, R4 = 2
It is not practical to try and list all of these events to find k.
Example 15.22 Rolling a Pair of Honest
Dice: Part 3
![Page 130: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/130.jpg)
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When events E and F are independent, the
probability that both occur is the product of
their respective probabilities; in other words,
Pr(E and F) = Pr(E) • Pr(F)
This is called the multiplication principle
for independent events.
Independent Events
![Page 131: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/131.jpg)
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Let F1, F2, F3, and F4 denote the events “first roll is not a one,” “second roll is not a one,” “third roll is not a one,” and “fourth roll is not a one,” respectively. These events are independent.
Let
F = F1 and F2 and F3 and F4
Example 15.22 Rolling a Pair of Honest
Dice
![Page 132: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/132.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 132 Copyright © 2010 Pearson Education, Inc.
Then
Pr(F1) = 5/6, Pr(F2) = 5/6
Pr(F3) = 5/6, Pr(F4) = 5/6
Multiplication principle for independent events gives the probability that you roll a number that is not one on all four rolls of the dice.
Pr(F) = 5/6 5/6 5/6 5/6 = (5/6)4 ≈ 0.482
Example 15.22 Rolling a Pair of Honest
Dice
![Page 133: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/133.jpg)
Excursions in Modern Mathematics, 7e: 15.1 - 133 Copyright © 2010 Pearson Education, Inc.
Imagine a game in which you roll an honest die four times. If at least one of your rolls comes up a one, you are a winner. Let E denote the event “you win”.
Find Pr(E).
Example 15.22 Rolling a Pair of Honest
Dice
![Page 134: 15.1 Random Experiments and Sample Spacesjga001/chapter 15 slides.pdf• For the sake of simplicity, we will concentrate on experiments for which there is only a finite set of outcomes,](https://reader034.vdocuments.us/reader034/viewer/2022042319/5f0834167e708231d420d977/html5/thumbnails/134.jpg)
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Let R1, R2, R3, and R4 denote the outcomes on roll 1, roll 2, roll 3, and roll 4.
Examples of rolls where you are a winner:
R1 = 4, R2 = 3, R3 = 1, R4 = 6
R1 = 1, R2 = 5, R3 = 2, R4 = 1
It would be tedious to write out all possible events in the sample space that correspond to at least one of the rolls comes up a one.
Example 15.22 Rolling a Pair of Honest
Dice
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Excursions in Modern Mathematics, 7e: 15.1 - 135 Copyright © 2010 Pearson Education, Inc.
The complement of E is the event that none of the rolls comes up one; that is, the event that you roll a number that is not one on all four rolls of the dice.
Therefore we use the previous example and the complement rule for probabilities to find the answer.
Example 15.22 Rolling a Pair of Honest
Dice
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Excursions in Modern Mathematics, 7e: 15.1 - 136 Copyright © 2010 Pearson Education, Inc.
Pr(E) = 1 – Pr(F) = 1 – 0.482 = 0.518
Example 15.22 Rolling a Pair of Honest
Dice
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Excursions in Modern Mathematics, 7e: 15.1 - 137 Copyright © 2010 Pearson Education, Inc.
15 Chances, Probabilities, and Odds
15.1 Random Experiments and Sample
Spaces
15.2 Counting Outcomes in Sample
Spaces
15.3 Permutations and Combinations
15.4 Probability Spaces
15.5 Equiprobable Spaces
15.6 Odds
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Excursions in Modern Mathematics, 7e: 15.1 - 138 Copyright © 2010 Pearson Education, Inc.
• In Example 15.15 discusses the fact that
Steve Nash (one of the most accurate free-
throw shooters in NBA history) shoots free
throws with a probability of p = 0.90.
• We can interpret this to mean that on the
average, out of every 100 free
throws,Nash is going to make 90 and miss
about 10, for a hit/miss ratio of 9/1 or
9 to 1.
Example 15.19 Odds of Making
Free Throws
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Excursions in Modern Mathematics, 7e: 15.1 - 139 Copyright © 2010 Pearson Education, Inc.
• This ratio of hits to misses gives what is
known as the odds of the event (in this
case Nash making the free throw).
Example 15.19 Odds of Making
Free Throws
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Excursions in Modern Mathematics, 7e: 15.1 - 140 Copyright © 2010 Pearson Education, Inc.
Let E be an arbitrary event. If F denotes
the number of ways that event E can
occur (the favorable outcomes or hits)
and U denotes the number of ways that
event E does not occur (the unfavorable
outcomes, or misses), then the odds of
(also called the odds in favor of) the
event E are given by the ratio:
F/U or F to U
ODDS
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Excursions in Modern Mathematics, 7e: 15.1 - 141 Copyright © 2010 Pearson Education, Inc.
the odds against the event E are given
by the ratio
U/F or U to F
Note: “the odds of E” is the same as “the
odds in favor of E”
ODDS
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Excursions in Modern Mathematics, 7e: 15.1 - 142 Copyright © 2010 Pearson Education, Inc.
Suppose that you are playing a game in
which you roll a pair of dice, presumably
honest. In this game, when you roll a “natural”
(i.e., roll a sum of 7 or sum of 11) you
automatically win. Find the odds of winning.
Example 15.26 Odds of Rolling a “Natural
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Excursions in Modern Mathematics, 7e: 15.1 - 143 Copyright © 2010 Pearson Education, Inc.
If we let E denote the event “roll a natural,”
we can check that out of 36 possible
outcomes 8 are favorable:
6 ways to “roll a 7” plus two ways to “roll an 11”
and the other 28 are unfavorable. It follows
that the odds of rolling a “natural” are
8/28=2/7 or 2 to 7
Example 15.26 Odds of Rolling a “Natural
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Excursions in Modern Mathematics, 7e: 15.1 - 144 Copyright © 2010 Pearson Education, Inc.
To convert odds into probabilities: If the
odds of E are F/U or F to U, then
Pr(E) = F/(F + U)
Converting Odds to Probability
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• Page 582, problem 61
Example
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Excursions in Modern Mathematics, 7e: 15.1 - 146 Copyright © 2010 Pearson Education, Inc.
• Page 582, problem 61
(a)3/8
(b)15/23
Example
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Excursions in Modern Mathematics, 7e: 15.1 - 147 Copyright © 2010 Pearson Education, Inc.
To convert probabilities into odds when the
probability is given in the form of a fraction:
If Pr(E) = A/B then the odds of E are:
(When the probability is given in decimal
form, the best thing to do is to first convert
the decimal form into fractional form.)
Converting Probability to Odds
ABAAB
A toor
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Excursions in Modern Mathematics, 7e: 15.1 - 148 Copyright © 2010 Pearson Education, Inc.
• Page 582, problem 60
Example
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Excursions in Modern Mathematics, 7e: 15.1 - 149 Copyright © 2010 Pearson Education, Inc.
• Page 582, problem 60
(a)3/8
(b)3/5
Example
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Excursions in Modern Mathematics, 7e: 15.1 - 150 Copyright © 2010 Pearson Education, Inc.
• There is a difference between odds as discussed in this section and the payoff odds posted by casinos or bookmakers in sports gambling situations.
• Suppose we read in the newspaper, for example, that the Las Vegas bookmakers have established that “the odds that the Boston Celtics will win the NBA championship are 5 to 2.”
Casinos and Bookmakers Odds
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Excursions in Modern Mathematics, 7e: 15.1 - 151 Copyright © 2010 Pearson Education, Inc.
• What this means is that if you want to bet in favor of the Celtics, for every $2 that you bet, you can win $5 if the Celtics win.
• The ratio 5 to 2 may be taken as some indication of the actual odds in favor of the Celtics winning, but several other factors affect payoff odds, and the connection between payoff odds and actual odds is tenuous at best.
Casinos and Bookmakers Odds
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Excursions in Modern Mathematics, 7e: 15.1 - 152 Copyright © 2010 Pearson Education, Inc.
This ratio may be taken as some indication of
the actual odds in favor of the Celtics
winning, but several other factors affect
payoff odds, and the connection between
payoff odds and actual odds is tenuous at
best.
Casinos and Bookmakers Odds