How Do You Calculate E(XY) for Dependent Variables with Given Observations?

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To calculate E(XY) for dependent random variables X and Y, the formula E(XY) can be derived directly from the sum of their products divided by the number of observations, which is 1060.84/21. It's important to note that for dependent variables, E(XY) does not equal E(X)E(Y). In this case, since no individual distributions for X and Y are provided, the focus remains solely on their product. Therefore, the calculation simplifies to using the provided sum and observation count. Understanding the nature of dependence is crucial in accurately determining expectations.
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Hi everyone,

I was searching an answer for E(XY), where X and Y are two dependent random variables, number of observations n=21 and Sum(x*y)= 1060.84. Can somebody help me?

It's not mentioned, but I think that each x and y of the distributions have the same probability to occur.
Thank you.
 
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Since you don't know anything about X and Y individually, you could think in terms of Z=XY. Then the estimate for E(Z) = 1064.84/21.
 
Expectation doesn't require independence so you can just do E(xy)=E(x)*E(y) or in this case, sum(XY)/n
 
randomafk said:
Expectation doesn't require independence so you can just do E(xy)=E(x)*E(y) or in this case, sum(XY)/n
This statement is misleading, E(XY) may not = E(X)E(Y) if they are dependent. However in the problem stated here, nothing in particular is known about X and Y, only the product, so E(X) and E(Y) are irrelevant.
 
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The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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