Conditional Probability

In summary, we have a conversation about finding the conditional probability of the union and intersection of two events given their individual probabilities. The equation for conditional probability is discussed, as well as the definitions of union and intersection. The simplified equation for finding the desired probability is given, along with a Venn diagram explanation. The conversation also includes a separate problem involving selecting a group of people with specific gender requirements and finding the probability of that selection. The solution to this problem is also provided.
  • #1
rickdundee
10
0
Given: P(A)= .4, P(B)=.3, P(A n B)=.11, P(C| not A)=.5

If P(C U A) = .66, then find P[(C U A) | (C n A)].

I have been trying to manipulate this thing for a while now with no luck.
Could you try and show the work if not that's alright, I'll work it out.
Thanks.
 
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  • #2
Well, there are a few ways to do it. What is the equation for conditional probabilty P(F|G), say?

Alternatively, what are the definitions of C u A and C n A? You want to find the probability that C u A occurs given that C n A has occured. Or, you want to find the probabilty that x is in the union of C and A given that it is in the intersection of C and A.

(This seems a bit easy; are you sure you've written the question correctly?)
 
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  • #3
That would be, P(F|G) = P(F n G)/P(G)

This is the correct question. It is just throwing me off that it is the conditional probability of the union and intersection of two events instead of just the conditional probability of two events.

I've gotten as far as: P[(C U A) n (C n A)] / P(C n A).

Is that correct.
 
  • #4
Yes. Now, can you simplify (C u A)n(C n A)? If you can't straight away, try drawing a venn diagram.

Remember, if x is in (C u A)n(C n A), then x is in C u A and C n A.
 
  • #5
P[(C U A) n (C n A)] = P(C n A)?
(C n A) is a subset of (C U A)?

So then the equation would be P(C n A) / P(C n A)?

Is that why you said it seems too easy or is this wrong?
 
  • #6
Yes, that's correct, which is why I thought you made a mistake writing the question up.

Intuitively, P[(C U A) | (C n A)] is the probability that C happens, or A happens, or they both happen, given that C and A have both happened, and so this must equal 1!
 
  • #7
Does it make a difference though that there are three events: A, B, and C. When drawing the Venn Diagram I also have to take event B into consideration also right, or that still does not make a difference.
 
  • #8
It doesn't make a difference. We are only considering A and C in this case. If you've drawn a Venn diagram with the 3 sets, then that's fine, but to tell whether x is in the intersection of A and C, we do not need to know whether it is in B or not.
 
  • #9
You are much appreciated. While I have you here and if your up for it, I doubt this is challenging for you but could you check this work.

There are 6 males and 4 females awaiting to see a teller at a bank.
It is the end of the day and there is only one teller, so only 4 of the people can be served one-at-a-time.

1) How many ways can four of the people be picked and served one at a time, if they must include two(2) men and two(2) women?

Solution: (6c2) * (4c2) = 90


2) If indeed the four people are picked randomly, what is the probability that the four will include two (2) men and two (2) women?

Solution: (6c2) * (4c2) / (10c4) = 3/7 = .428571
 

1. What is conditional probability?

Conditional probability is a mathematical concept that measures the likelihood of an event occurring given that another event has already occurred. It is used to calculate the probability of one event happening given that another event has already taken place.

2. How is conditional probability calculated?

Conditional probability is calculated by dividing the probability of the joint occurrence of two events by the probability of the second event. This can be written as P(A|B) = P(A∩B) / P(B), where P(A|B) is the conditional probability of event A given event B, P(A∩B) is the joint probability of events A and B, and P(B) is the probability of event B.

3. What is the difference between conditional probability and unconditional probability?

Unconditional probability, also known as marginal probability, measures the likelihood of an event occurring without any additional information. On the other hand, conditional probability takes into account additional information and calculates the likelihood of an event happening given that another event has already occurred.

4. How is conditional probability used in real life?

Conditional probability is used in various fields such as statistics, finance, and science to make predictions and decisions based on available information. For example, it can be used to predict the likelihood of a medical treatment being successful for a patient with a specific condition, or to determine the probability of a stock market trend based on current economic conditions.

5. What are some common misconceptions about conditional probability?

Some common misconceptions about conditional probability include assuming that events are independent when they are actually dependent, and confusing conditional probability with causation. It is important to carefully consider the relationship between events and the available information before calculating conditional probability to avoid these misconceptions.

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