How to find E(XY) when X and Y are NOT indepdant?

  • Thread starter laura_a
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In summary, the conversation is about finding the expected value of X and Y given a joint probability density function, where X and Y are not independent. The suggested method is to integrate xy against the pdf, or use the definition of an expectation. It is also mentioned that the function is not symmetric about zero, so the expected value of XY cannot be simplified to 0.
  • #1
laura_a
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Homework Statement



I have a joint pdf f_{XY}(x,y) = (2+x+y)/8 for -1<x<1 and -1<y<1

Homework Equations



I have to work out E(XY) but I have previously worked out that X and Y are NOT independant (that is f_{XY}(0,1) doesn't equal f_X{0}*f_Y{1}). I am using maxima so I don't need help with any integration, I just need to know what formula because I've read that E(XY) = E(X)E(Y) only when they're indepdant... so what happens when they're not?
 
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  • #2
You integrate xy against the pdf. Do you not have the textbook?
 
  • #3
No, there is no textbook for this, I have bought some books, but none of them are written for people who aren't the best at statistics. I have no idea what you mean, isn't there an easier way using E(X) and E(Y) which I already have?
 
  • #4
No, there's not. Is this for a class?
 
  • #5
Oh goody double posting! Laura you now have two people telling you the same thing-- integrate. I don't know why you had to start two threads on the same topic instead of just being patient.
 
  • #6
You can use the definition of an expectation.
E(XY) = [tex]\oint\oint[/tex]x*y*f(x,y) dy dx
Or you could argue that since the function is symmetric about 0 and the intervals [-1, 1] are centred about 0 that E(XY) = 0
 
  • #7
The density isn't symmetric about zero.

Laura, for any joint continuous distribution, whether or not [tex] X, Y [/tex] are independent, you can find [tex] E[XY] [/tex] as

[tex]
\iint xy f(x,y) \, dxdy
[/tex]
 

FAQ: How to find E(XY) when X and Y are NOT indepdant?

What is the formula for finding E(XY) when X and Y are not independent?

The formula for finding E(XY) when X and Y are not independent is E(XY) = E(X)E(Y|X), where E(X) is the expected value of X and E(Y|X) is the conditional expected value of Y given X.

How do you calculate E(X) when X and Y are not independent?

To calculate E(X), you can use the formula E(X) = ∑x P(X=x) * x, where x is the value of X and P(X=x) is the probability of X taking that value. This formula remains the same regardless of whether X and Y are independent or not.

What is the relationship between E(XY) and E(X)E(Y) when X and Y are not independent?

When X and Y are not independent, the relationship between E(XY) and E(X)E(Y) is given by the formula E(XY) = E(X)E(Y) + Cov(X,Y), where Cov(X,Y) is the covariance between X and Y. This means that E(XY) is equal to the product of the expected values of X and Y plus their covariance.

Can E(XY) be negative when X and Y are not independent?

Yes, E(XY) can be negative when X and Y are not independent. This can happen when the covariance between X and Y is negative, indicating a negative relationship between the two variables.

Why is it important to consider the independence of X and Y when calculating E(XY)?

It is important to consider the independence of X and Y when calculating E(XY) because if they are not independent, the formula for finding E(XY) changes and the result may not accurately reflect the relationship between the two variables. Taking into account the dependence between X and Y allows for a more accurate calculation of their expected value together.

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