Chapter 17
Thinking about Chance
In this chapter
•
Introduction to probability
•
Probability rules
•
Independence
•
Probability from a contingency table
Introduction to probability
We will look at situations with more than one outcome for which we cannot know the
outcome with certainty. Therefore, we will deal with probability.
Sample space
– the set of all possible outcomes from an experiment
Example
Identify the sample space for each of the following
•
Roll a die
}
6
,
5
,
4
,
3
,
2
,
1
{
space
sample
=
•
Flip a coin
}
T
H,
{
space
sample
=
The above examples illustrate a common way to denote a sample space. Also notice that
when you roll a die you get a discrete quantitative variable and when you flip a coin you
get a qualitative variable. These are the types of data we will deal with in the next few
chapters. We already talked about probability for a continuous quantitative variable in
chapter 13 when looking at the area under a density curve.
Event
– a subset of the sample space
Example
Suppose you roll a die and event A is defined by getting an even number. We would
represent this event as follows.
}
6
,
4
,
2
{
A
=
Probability
(of an event A) – denoted
)
(
A
P
, is the expected proportion of occurrences
of A if the experiment were performed a large number of times (keep in mind A is just an
example, we could call the event whatever we wanted)
outcomes
of
#
Total
event
in the
outcomes
of
#
event
an
of
y
Probabilit
=
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View Full DocumentWe will look at a couple counting principal problems in order to understand how to
identify the number of outcomes when there are a series of operations in an experiment.
Example 1
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 Fall '10
 Bradley,W
 Statistics, Conditional Probability, Probability, Probability theory, Probability space

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