Intersection of unindependent events

AI Thread Summary
The discussion focuses on proving the inequality -0.25 <= P(X ∩ Y) - P(X)P(Y) <= 0.25 for any events X and Y. It acknowledges that if X and Y are independent, the result is 0, but challenges arise when considering dependent events. The user is struggling to find a solution and suspects there may be a trick involved. They also explore scenarios of total dependence and anti-dependence, questioning how these affect the inequality. The conversation highlights the complexity of calculating probabilities for non-independent events.
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Homework Statement



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-0.25 <= P( X \cap Y ) - P( X )P( Y ) <= 0.25

for any events X, Y


Homework Equations


P( X \cap Y ) = P( X )P( Y | X )
Bayes' theorem
Anything I missed?


The Attempt at a Solution



Obviously if X and Y are independent
P( X \cap Y ) = P( X )P( Y )
so
P( X \cap Y ) - P( X )P( Y ) = 0

but if they are not then I hit a wall. I've done pages of math but I go round in circles. I think there's some trick but I can't figure it out. Can anyone tell me what I'm missing?
 
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If the events are totally independent, as you surmised, the result is 0.

What if, they are totally dependent, that is, P(X)=P(Y)?
Or if they are totally anti-dependent, that is, P(X)=1-P(Y)?
 
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