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In [link] , we considered conditional probabilities. In some examples, the probability of an event changed when additional information was provided. For instance, the probability of obtaining a king from a deck of cards, changed from to , when we were given the condition that a face card had already shown. This is not always the case. The additional information may or may not alter the probability of the event. For example consider the following example.
A card is drawn from a deck. Find the following probabilities.
Clearly, .
To find , we reason as follows:
Since a red card has shown, there are only twenty six possibilities. Of the 26 red cards, there are two kings. Therefore,
.
The reader should observe that in the above example,
In other words, the additional information, a red card has shown, did not affect the probability of obtaining a king. Whenever the probability of an event is not affected by the occurrence of another event , and vice versa, we say that the two events and are independent . This leads to the following definition.
Two Events and are independent if and only if at least one of the following two conditions is true.
Next, we need to develop a test to determine whether two events are independent.
We recall the conditional probability formula.
Multiplying both sides by , we get
Now if the two events are independent, then by definition
Substituting,
We state it formally as follows.
The table below shows the distribution of color-blind people by gender.
Male(M) | Female(F) | Total | |
Color-Blind(C) | 6 | 1 | 7 |
Not Color-Blind (N) | 46 | 47 | 93 |
Total | 52 | 48 | 100 |
Where represents male, represents female, represents color-blind, and not color-blind. Use the independence test to determine whether the events color-blind and male are independent.
According to the test, and are independent if and only if .
and
Clearly
Therefore, the two events are not independent. We may say they are dependent.
In a survey of 100 women, 45 wore makeup, and 55 did not. Of the 45 who wore makeup, 9 had a low self-image, and of the 55 who did not, 11 had a low self-image. Are the events "wearing makeup" and "having a low self-image" independent?
Let be the event that a woman wears makeup, and the event that a woman has a low self-image. We have
, and
In order for two events to be independent, we must have
Since
The two events "wearing makeup" and "having a low self-image" are independent.
A coin is tossed three times, and the events , and are defined as follows:
: The coin shows a head on the first toss.
: At least two heads appear.
: Heads appear in two successive tosses.
Determine whether the following events are independent.
To make things easier, we list the sample space, the events, their intersections and the corresponding probabilities.
, or
, or
, or
,
, or
In order for and to be independent, we must have
.
But
Therefore, and are not independent.
and will be independent if
.
Since
and are not independent.
We look at
Therefore, and are independent events.
The probability that Jaime will visit his aunt in Baltimore this year is , and the probability that he will go river rafting on the Colorado river is . If the two events are independent, what is the probability that Jaime will do both?
Let be the event that Jaime will visit his aunt this year, and be the event that he will go river rafting.
We are given and , and we want to find .
Since we are told that the events and are independent,
Given . If and are independent, find .
If and are independent, then by definition
Therefore,
Given , . Find .
By definition
Substituting, we have
Therefore,
Given , , if and are independent, find .
The addition rule states that
Since and are independent,
We substitute for in the addition formula and get
By letting , and substituting values, we get
Therefore, .
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