Explain in simple words the difference between the Causal and Correlated metric types.

Jaelyn Payne 2022-10-15 Answered
Explain in simple words the difference between the Causal and Correlated metric types.
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Answers (1)

Miah Scott
Answered 2022-10-16 Author has 19 answers
causations and correlation (connections) can exist simultaneously, relationship doesn't infer causation.
Causation unequivocally applies to situations where activity A causes result B. Then again, connections(correlation) is essentially a relationship. Activity A connects with Action B-however one occasion doesn't be guaranteed to make the other occasion occur.
Relationship(correlation)and causation are frequently confounded in light of the fact that the human psyche likes to observe designs in any event, when they don't exist. We regularly manufactures these examples when two factors have all the earmarks of being so firmly related that one is reliant upon the other. That would suggests a circumstances and logicals results relationships where the reliant occasion is the aftereffect of an autonomous occasion.
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Then the often stated line of correlation does not imply causation is simply! Q P.
It is also true that causation does not imply correlation. So! Q P
But ( P Q ) ( Q P ) is a tautology.
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asked 2022-10-07
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asked 2022-09-17
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The statement:
Suppose that a patient tests positive for a disease affecting 1% of the population. For a patient who has the disease, there is a 95% chance of testing positive, and for a patient who doesn't has the disease, there is a 95% chance of testing negative. The patient gets a second, independent, test done, and again tests positive. Find the probability that the patient has the disease.
The problem:
I can solve this problem, but I'm unable to understand what is wrong with the following:
Let T i be the event that the patient tests positive in the i-th test, and let D be the event that the patient has the disease.
The problem says that P ( T 1 , T 2 ) = 0.95 2 0.01 + 0.05 2 0.99 = 0.0115, because the tests are independent.
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P ( T 1 , T 2 ) = 0.95 2 0.01 + 0.05 2 0.99 = 0.0115
Replacing, and assuming conditional independence given D, we have:
P ( T 1 , T 2 ) = 0.95 2 0.01 + 0.05 2 0.99 = 0.0115
This is the correct result, but now let's consider that:
P ( T 1 , T 2 ) = P ( T 1 ) 2
We know that P ( T 1 , T 2 ) = P ( T 1 ) 2 for all i because of symmetry, so we have P ( T 1 , T 2 ) = P ( T 1 ) 2 . Again, by law of total probability:
P ( T 1 ) = 0.95 0.01 + 0.05 0.99 0.059
P ( T 1 ) = 0.95 0.01 + 0.05 0.99 0.059
So we have:
P ( T 1 , T 2 ) = P ( T 1 ) 2 0.059 2 0.003481
The second approach is wrong, but it seems legitimate to me, and I'm unable to find what's wrong.
Thank's for your help, you make self studying easier.
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