Independent Probability Question

In summary, the conversation discusses the independence of outcomes A and B in a sample space that can be represented as the union of A and B. The speaker provides an example of A and B^c and calculates the probability of their intersection, concluding that they are not independent. They also mention difficulties in applying this reasoning to other outcomes and evaluating probabilities. Another speaker suggests using a different calculation to determine that A and B^c are not independent, and correctly calculates the probability of their intersection. The conversation ends with a discussion on the calculation of P(A \cup B^c).
  • #1
tomtom690
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Homework Statement


Hello, I just want to know if I am going about this the right way.
A and B are outcomes of a random experiment in a sample space [tex]\Omega[/tex] such that [tex]\Omega[/tex] = A[tex]\cup[/tex]B. P(A) = 0.8 and P(B) = 0.5 Study if A and B, A and B[tex]^{c}[/tex], A[tex]^{c}[/tex] and B, and A[tex]^{c}[/tex] and B[tex]^{c}[/tex] are independent outcomes. Also, evaluate P(A[tex]\cup[/tex]B[tex]^{c}[/tex]) etc.


Homework Equations





The Attempt at a Solution


For the first three, I have used the same reasoning. I shall give an example of A and B[tex]^{c}[/tex].
Since [tex]\Omega[/tex] = A[tex]\cup[/tex]B then A[tex]\cup[/tex]B[tex]^{c}[/tex]=A
Now, let x = P(A[tex]\cup[/tex]B[tex]^{c}[/tex])=P(A)+P(B[tex]^{c}[/tex])-P(A[tex]\cap[/tex]B[tex]^{c}[/tex])

Now if A and B[tex]^{c}[/tex] are independent, then their intersection is the same as multiplying them together. So P(A[tex]\cap[/tex]B[tex]^{c}[/tex]) = 0.8*0.5 = 0.4
This means that P(A[tex]\cup[/tex]B[tex]^{c}[/tex]) = 0.9 [tex]\neq[/tex] P(A) = 0.8, so they are not independent.

However I am having difficulty applying this reasoning (if correct!) to the final one. And then the evaluation part seems too easy, as I have already said in this working out what they are equal to.

Thanks.
 
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  • #2
[tex]P(A^c \cap B^c) = 1 - P(A \cup B) = 0[/tex]

whereas

[tex]P(A^c)P(B^c) = (0.2)(0.5) \neq 0[/tex]

so [itex]A^c[/itex] and [itex]B^c[/itex] are not independent.
 
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  • #3
As for the other questions, I calculated

[tex]P(A \cup B) = P(A) + P(B) - P(A \cap B)[/tex]

so

[tex]1 = 0.8 + 0.5 - P(A \cap B)[/tex]

which means that

[tex]P(A \cap B) = 0.3[/tex]

Now use

[tex]P(A) = P(A \cap B) + P(A \cap B^c)[/tex]

to establish that

[tex]P(A \cap B^c) = 0.5[/tex]

which disagrees with your calculation.

[Edit] Oh wait, I see what you did. Yes, I think your way is OK, too.

If you use my calculation, you would observe that

[tex]P(A)P(B^c) = (0.8)(0.5) = 0.4[/tex]

which does not equal [itex]P(A \cap B^c)[/itex], so [itex]A[/itex] and [itex]B^c[/itex] are not independent.

You can then calculate

[tex]P(A \cup B^c) = P(A) + P(B^c) - P(A \cap B^c) = 0.8[/tex]
 
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FAQ: Independent Probability Question

What is Independent Probability Question?

Independent Probability Question refers to a type of probability problem where the outcome of one event does not affect the outcome of another event. In other words, the two events are independent of each other and the probability of one event occurring does not change based on whether or not the other event occurs.

How do you calculate the probability in an Independent Probability Question?

To calculate the probability in an Independent Probability Question, you multiply the probabilities of each event together. This is known as the multiplication rule for independent events. For example, if Event A has a probability of 1/4 and Event B has a probability of 1/2, the probability of both events occurring together is 1/4 x 1/2 = 1/8.

Can independent events have a probability of 0 or 1?

Yes, independent events can have a probability of 0 or 1. A probability of 0 means that the event will never occur, while a probability of 1 means that the event will always occur. For example, if you flip a coin and roll a dice, the probability of getting a heads and rolling a 6 is 1/2 x 1/6 = 1/12, which is not a 0 or 1 probability.

How do you know if events are independent?

Events are considered independent if the outcome of one event does not affect the outcome of the other event. In other words, the probability of one event occurring does not change based on whether or not the other event occurs. For example, flipping a coin and rolling a dice are independent events because the result of one event does not impact the result of the other event.

Can independent events become dependent?

Yes, independent events can become dependent if there is a hidden relationship between them that affects their outcomes. For example, if you are drawing cards from a deck without replacement, the probability of drawing an ace on the first draw is 4/52. However, if you do not replace the card and draw again, the probability of drawing an ace on the second draw is now 3/51 because there is one less ace in the deck. This shows that the events are no longer independent and the outcome of the first draw affects the probability of the second draw.

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