Intersection of unindependent events

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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. The user correctly identifies that for independent events, P(X ∩ Y) equals P(X)P(Y), resulting in a difference of zero. However, the challenge arises when considering dependent events, where the user seeks clarification on how to approach the proof. The discussion highlights the need for a deeper understanding of probability concepts, particularly in relation to dependent and anti-dependent events.

PREREQUISITES
  • Understanding of probability theory, specifically joint and conditional probabilities
  • Familiarity with Bayes' theorem
  • Knowledge of independent and dependent events in probability
  • Ability to manipulate inequalities in mathematical proofs
NEXT STEPS
  • Study the properties of joint probability distributions
  • Learn about the implications of dependent and independent events in probability
  • Explore advanced applications of Bayes' theorem
  • Investigate mathematical proofs involving inequalities in probability
USEFUL FOR

Students studying probability theory, mathematicians focusing on statistical methods, and educators teaching concepts of independence and dependence in events.

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Homework Statement



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-0.25 <= P( X [tex]\cap[/tex] Y ) - P( X )P( Y ) <= 0.25

for any events X, Y


Homework Equations


P( X [tex]\cap[/tex] 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 [tex]\cap[/tex] Y ) = P( X )P( Y )
so
P( X [tex]\cap[/tex] 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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