1 Business Statistics - QBM117 Assigning probabilities to events.
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Transcript of 1 Business Statistics - QBM117 Assigning probabilities to events.
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Business Statistics - QBM117
Assigning probabilities to events
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Objectives
To define probability;
To describe the relationship between randomness and probability;
To define a random experiment;
To revise the methods of assigning probabilities;
To introduce some of the probability rules.
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What is probability?
Probability is a numerical measure of uncertainty;
It is a number that conveys the strength of our belief in the occurrence of an uncertain event;
It is often associated with gambling;
It is now an indispensable tool in the analysis of situations which involve uncertainty.
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Randomness
What is the mean age of all the students in this class?
From a random sample of 10 students we can estimate the mean age of all the students.
How can the mean, based on only a sample of 10, be an accurate estimate of the population mean, ?
A second random sample would most likely produce a different value for the mean.
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This is due to sampling variability.
Why is this not a problem?
Chance behaviour is unpredictable in the short term but has a regular and predictable pattern in the long term.
For example, consider the experiment of tossing a coin
The results cannot be predicted in advance but there is a pattern which emerges, only after repeated sampling.
This is the basis for probability.
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Randomness and probability
A phenomenon is called random if the individual outcomes are uncertain, but there is a regular distribution of outcomes in a large number of repetitions.
The probability of any outcome of a random phenomenon is the proportion of times the outcome would occur in a very long series of repetitions.
Probability is an idealisation based on what would happen in an infinitely long series of trials.
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Random experimentsWe begin our study of probability by considering the random experiment, as this process generates the uncertain outcomes to which we assign probabilities.
A random experiment is any well defined procedure that results in one of a number of possible outcomes.
The outcome that occurs, cannot be predicted with certainty.
For example, rolling a die and observing the number uppermost on the die.
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Important
The actual outcome of a random experiment cannot be determined in advance.
We can only talk about the probability that a particular outcome will occur.
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Example
If we roll a die, the uppermost face can be a
1, 2, 3, 4, 5 or a 6. These numbers form the
sample space.
Sample Space
1
3
4
5
6
2
is the complete list of all the possible outcomes of an experiment.
The sample space
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Example
Rolling a die with an even number
uppermost can be an event.
Sample Space
1 3 5
2 4 6
Events
The desired outcome or outcomes from the sample space.
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Example
In a family of two children the
event of there being at least
one boy is:
Sample Space
girl / girl boy / boy
girl / boy
boy / girl
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Example
If we roll a die the uppermost face can be
either a 1, 2, 3, 4, 5 or a 6.
These numbers form the sample space.
The sample space can be written as:
6 5, 4, 3, 2, 1, S
Notation
Events can be described in written form, provided the label is defined before hand.
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2 1, B
Example
When rolling a die, let A
equal the even numbers.
6 4, 2, A
When rolling a die, let B
equal the numbers less than three.
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The probability of an event is defined as the number of members in an event divided by the number of members in the sample space.
Probability of an event
SnAn
AP
Where n(A) is the number of members in the event A and n (S ) is the number of members in the sample space.
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Example
When rolling a die:
, , , , , S 654321
6 4, 2, A {The event of an even number}
5.063
SnAn
AP
The probability of A is:
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Example
On a particular day a statistics lecture has 260 students attending. There are 65 mature aged students in the lecture.
If one student is selected at random, what is the probability that a mature age student is selected?
25.026065
SnMn
MP
S ={all the students in the lecture}
M = {mature age students in the lecture}
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A~ cAAAA
The complement of an event are those members of the sample space that are not contained within the event.
Complement of an event
Complement rule
APAP 1
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Example
When rolling a die, event A is defined
as the even numbers. Find :ASample Space
A2 4 6
1 3 5 A
5.06
31
)(1)(
APAP
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The intersection of two events are the members that are common to both events.
The word used to represent the intersection of two events is:
AND
Intersection
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Example
When rolling a die, let A
be the event an even number
is uppermost. 6 4, 2, A
When rolling a die, let B be the event a
number less than three is uppermost.
2 1, B
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Sample Space
Find the intersection of A and B.
1 3 5
2 4 6
It’s just me, all alone…
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Find the probability of A and B.
1667.06
1
and and
Sn
BAnBAP
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Example
Let A be defined as the students with2 blue eyes in a lecture theatre and B be defined as the students with two browneyes in the lecture theatre.
Then the probability students will have two blue and two brown eyes in the lecture theatre is ...
Put your hand up if you
look like me!
0 and BAP
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Two events are mutually exclusive when they have no members in common. (They don’t share any members)
Mutually exclusive events
0 and
0) and(
BAP
BAn
A B
Example
A is the event an even number is uppermost on a die
B is the event an odd number is uppermost on die
2 4 6 1 3 5
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The union of two events are those members that are in one event or the other event or in both.
The word used to represent the union of two events is:
OR
Union of events
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Example
When rolling a die, let X be the
event an odd number is uppermost.
5 3, 1, X
When rolling a die, let Y be the event
a number less than five is uppermost.
2,3,4 1, Y
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Sample Space
Find the probability of X or Y.
1 3 5
2 4 6
All except for me !!
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Therefore the probability of X or Y is
8333.06
5
or or
Sn
YXnYXP
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We do not always know the members of each event,rather we only know the probabilities of these events.
In such cases, there is a formula that enables us to findthe probability of A or B:
BAPBPAPBAP andor
Addition formula
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Sample Space
A BACounted once
with P(A) BCounted twice
with P(B)
Intersection
Addition formula
BAPBPAPBAP andor
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Example
In a population the probability of being female is 0.6 and the probability of being aged 30 and over is 0.4. The probability of being female and aged 30 and over is 0.2. Find the probability of being either female or aged 30 and over.
Let F represent the females in the populationLet O represent the people aged 30 and over
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Example
In a population the probability of being female is 0.6 and the probability of being aged 30 and over is 0.4. The probability of being female and aged 30 and over is 0.2. Find the probability of being either female or aged 30 and over.
6.0FP 40.OP 2.0and OFP
8.0
2.04.06.0
andor
OFPOPFPOFP
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Reading for next lecture
Chapter 4 sections 4.4 - 4.5
Exercise to be completed before next lecture
S&S 4.5 4.7 4.13 4.15 4.69