Expectation of X*Y= E(XY)

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In summary, the conversation is about finding the expected value of the product of two dependent random variables, where the number of observations is 21 and the sum of the products is 1060.84. It is mentioned that the distributions of X and Y have the same probability to occur, and the solution is to think in terms of Z=XY and then calculate the expected value of Z using the given information. The statement about independence is clarified, stating that expectation does not require independence and can simply be calculated using the sum of the products divided by the number of observations.
  • #1
James1990
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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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  • #2
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.
 
  • #3
Expectation doesn't require independence so you can just do E(xy)=E(x)*E(y) or in this case, sum(XY)/n
 
  • #4
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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  • #5


Hello there,

Thank you for sharing your question about the expectation of X*Y. The expectation of a product of two random variables, X and Y, is defined as E(XY) = E(X)E(Y), where E(X) and E(Y) are the individual expectations of X and Y, respectively. In your case, since X and Y are dependent variables, their joint distribution needs to be considered in order to calculate E(XY). Without further information about the joint distribution, it is difficult to provide a specific answer. However, based on the given information of n=21 and Sum(x*y)= 1060.84, it seems like you might have the necessary data to calculate the covariance between X and Y, which can then be used to calculate E(XY). I would suggest consulting a statistics textbook or seeking help from a statistician for a more precise answer. Best of luck with your research!
 

1. What is the meaning of "expectation of X*Y"?

The expectation of X*Y, also known as the expected value of X*Y, is a measure of the average value that we would expect to obtain from the product of two random variables X and Y. In other words, it is the sum of all possible outcomes of X*Y multiplied by their respective probabilities.

2. How is the expectation of X*Y calculated?

The expectation of X*Y can be calculated using the formula E(XY) = ∑∑x*y*p(x,y), where x and y represent the possible values of X and Y, and p(x,y) represents the joint probability of X and Y taking on those values. This formula can also be extended to continuous random variables using integrals.

3. What is the relationship between expectation of X*Y and covariance?

The expectation of X*Y is directly related to the covariance of X and Y. Specifically, the covariance of X and Y is equal to the expectation of X*Y minus the product of the expectations of X and Y. In other words, it measures how much two random variables vary together.

4. Can the expectation of X*Y be negative?

Yes, the expectation of X*Y can be negative. This can occur when the product of two random variables has a negative value, or when the joint probability of X and Y taking on certain values is negative. However, in most cases, the expectation of X*Y will be a positive value.

5. How is the expectation of X*Y used in statistical analysis?

The expectation of X*Y is a useful tool in statistical analysis as it allows us to calculate the expected value of a product of two random variables. This can be used to make predictions and draw conclusions about the relationship between the two variables. Additionally, it is often used in the calculation of other important measures such as correlation and regression coefficients.

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