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![Page 1: 1 1 Slide Introduction to Probability Assigning Probabilities and Probability Relationships Chapter 4 BA 201.](https://reader036.fdocuments.in/reader036/viewer/2022062309/5697bf7b1a28abf838c83b02/html5/thumbnails/1.jpg)
1 1 Slide Slide
Introduction to ProbabilityAssigning Probabilities and Probability
Relationships
Chapter 4BA 201
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2 2 Slide Slide
ASSIGNING PROBABILITIES
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3 3 Slide Slide
Assigning Probabilities
Basic Requirements for Assigning Probabilities
1. The probability assigned to each experimental outcome must be between 0 and 1, inclusively.
0 < P(Ei) < 1 for all i
where:Ei is the ith experimental outcome and P(Ei) is its probability
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4 4 Slide Slide
Assigning Probabilities
Basic Requirements for Assigning Probabilities
2. The sum of the probabilities for all experimental outcomes must equal 1.
P(E1) + P(E2) + . . . + P(En) = 1
where:n is the number of experimental outcomes
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5 5 Slide Slide
Assigning Probabilities
Classical Method
Relative Frequency Method
Subjective Method
Assigning probabilities based on the assumption of equally likely outcomes
Assigning probabilities based on experimentation or historical data
Assigning probabilities based on judgment
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6 6 Slide Slide
Classical Method
If an experiment has n possible outcomes, the classical method would assign a probability of 1/n to each outcome.
Experiment: Rolling a die
Sample Space: S = {1, 2, 3, 4, 5, 6}
Probabilities: Each sample point has a1/6 chance of occurring1/6+1/6+1/6+1/6+1/6+1/6 = 1
Example: Rolling a Die
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7 7 Slide Slide
Relative Frequency Method
Number ofPolishers Rented
Numberof Days
01234
4 61810 2
Lucas Tool Rental would like to assign probabilitiesto the number of car polishers it rents each day. Office records show the following frequencies of dailyrentals for the last 40 days.
Lucas Tool Rental
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8 8 Slide Slide
Each probability assignment is given by dividing
the frequency (number of days) by the total frequency
(total number of days).
Relative Frequency Method
4/404/40
ProbabilityNumber of
Polishers RentedNumberof Days
01234
4 61810 240
0.10 0.15 0.45 0.25 0.051.00
Example: Lucas Tool Rental
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9 9 Slide Slide
Subjective Method
When economic conditions and a company’s circumstances change rapidly it might be inappropriate to assign probabilities based solely on historical data. We can use any data available as well as our experience and intuition, but ultimately a probability value should express our degree of belief that the experimental outcome will occur.
The best probability estimates often are obtained by combining the estimates from the classical or relative frequency approach with the subjective estimate.
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10 10 Slide Slide
Subjective Method
An analyst made the following probability estimates.
Exper. OutcomeNet Gain or Loss Probability(10, 8)(10, -2)(5, 8)(5, -2)(0, 8)(0, -2)(-20, 8)(-20, -2)
$18,000 Gain $8,000 Gain $13,000 Gain $3,000 Gain $8,000 Gain $2,000 Loss $12,000 Loss $22,000 Loss
0.200.080.160.260.100.120.020.06
Example: Bradley Investments
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11 11 Slide Slide
An event is a collection of sample points.
The probability of any event is equal to the sum of the probabilities of the sample points in the event.
If we can identify all the sample points of an experiment and assign a probability to each, we can compute the probability of an event.
Events and Their Probabilities
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12 12 Slide Slide
Events and Their Probabilities
Event M = Markley Oil Profitable
M = {(10, 8), (10, -2), (5, 8), (5, -2)}
P(M) = P(10, 8) + P(10, -2) + P(5, 8) + P(5, -2)
= 0.20 + 0.08 + 0.16 + 0.26
= 0.70
Bradley Investments
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13 13 Slide Slide
Events and Their Probabilities
Event C = Collins Mining Profitable
C = {(10, 8), (5, 8), (0, 8), (-20, 8)}
P(C) = P(10, 8) + P(5, 8) + P(0, 8) + P(-20, 8)
= 0.20 + 0.16 + 0.10 + 0.02
= 0.48
Example: Bradley Investments
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14 14 Slide Slide
PROBABILITY RELATIONSHIPS
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15 15 Slide Slide
Some Basic Relationships of Probability
There are some basic probability relationships that
can be used to compute the probability of an event
without knowledge of all the sample point probabilities.
Complement of an Event
Intersection of Two Events
Mutually Exclusive Events
Union of Two Events
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16 16 Slide Slide
The complement of A is denoted by Ac.
The complement of event A is defined to be the event consisting of all sample points that are not in A.
Complement of an Event
Event A AcSampleSpace S
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17 17 Slide Slide
The union of events A and B is denoted by A B
The union of events A and B is the event containing all sample points that are in A or B or both.
Union of Two Events
SampleSpace SEvent A Event B
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18 18 Slide Slide
Union of Two Events
Event M = Markley Oil Profitable
Event C = Collins Mining Profitable
M C = Markley Oil Profitable or Collins Mining Profitable (or both)
M C = {(10, 8), (10, -2), (5, 8), (5, -2), (0, 8), (-20, 8)}
P(M C) = P(10, 8) + P(10, -2) + P(5, 8) + P(5, -2) + P(0, 8) + P(-20, 8)
= 0.20 + 0.08 + 0.16 + 0.26 + 0.10 + 0.02
= 0.82
Example: Bradley Investments
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19 19 Slide Slide
The intersection of events A and B is denoted by A
The intersection of events A and B is the set of all sample points that are in both A and B.
SampleSpace SEvent A Event B
Intersection of Two Events
Intersection of A and B
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20 20 Slide Slide
Intersection of Two Events
Event M = Markley Oil Profitable
Event C = Collins Mining Profitable
M C = Markley Oil Profitable and Collins Mining Profitable
M C = {(10, 8), (5, 8)}
P(M C) = P(10, 8) + P(5, 8)
= 0.20 + 0.16
= 0.36
Bradley Investments
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21 21 Slide Slide
Mutually Exclusive Events
Two events are said to be mutually exclusive if the events have no sample points in common.
Two events are mutually exclusive if, when one event occurs, the other cannot occur.
SampleSpace SSampleSpace SEvent A Event B
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22 22 Slide Slide
Mutually Exclusive Events
If events A and B are mutually exclusive, P(A B = 0.
The addition law for mutually exclusive events is:
P(A B) = P(A) + P(B)
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23 23 Slide Slide
PRACTICE PROBABILITY RELATIONSHIPS
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24 24 Slide Slide
Practice
Based on the Venn diagram, describe the following:
A B
C
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25 25 Slide Slide
Practice
Out of forty students, 14 are taking English and 29 are taking Chemistry. Five students are in both classes.
Draw a Venn Diagram depicting this relationship.
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26 26 Slide Slide
Practice
Students are either undergraduate students or graduate students.
Draw a Venn Diagram depicting this relationship.
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27 27 Slide Slide
Scenario
Outcome Probability
O1 0.10
O2 0.30
O3 0.05
O4 0.15
O5 0.20
O6 0.05
O7 0.10
O8 0.05
Event Outcomes
E1 O1, O3
E2 O1, O4, O5, O6
E3 O2
E4 O7, O8
E5 O4, O5, O7
E6 O1, O7
A statistical experiment has the following outcomes, along with their probabilities and the following events, with the corresponding outcomes.
Outcomes Events
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28 28 Slide Slide
Probabilities of Events
What is the probability of E1 [P(E1)]?
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29 29 Slide Slide
Probabilities of Events
What is the probability of E2 [P(E2)]?
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30 30 Slide Slide
Probabilities of Events
What is the probability of E3 [P(E3)]?
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31 31 Slide Slide
Probabilities of Events
What outcomes are in the complement of E2 [E2c]?
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32 32 Slide Slide
Probabilities of Events
What outcomes are in the union of E3 and E4? (E3 ⋃ E4)? What is the probability of E3 ⋃ E4 [P(E3 ⋃ E4)]?
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33 33 Slide Slide
Probabilities of Events
What outcomes are in the intersection of E2 and E5 (E2 ⋂ E5)? What is the probability of E2 ⋂ E5 [P(E2 ⋂ E5)]?
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34 34 Slide Slide
Probabilities of Events
Based on the available information, are E2 and E4 mutually exclusive? What is the probability of E2 ⋂ E4 [P(E2 ⋂ E4)]? What is the probability of E2 ⋃ E4 [P(E2 ⋃ E4)]?
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35 35 Slide Slide