Expected value of X and Y, E[XY] for uniform random variables

In summary, the question is asking if it is possible to determine the covariance between two variables, X and Y, where Y is equal to X squared and X is uniformly distributed between -1 and 1. Using the formula for covariance, it is possible to calculate the expected value of XY, which can be further simplified using the fact that Y is equal to X squared. This means that Y and X squared are not just have the same distribution, but are in fact equal to each other.
  • #1
Dustinsfl
2,281
5

Homework Statement


If ##X\sim\mathcal{U}(-1,1)## and ##Y = X^2##, is it possible to determine to ##cov(X, Y)##?

Homework Equations


\begin{align}
f_x &=
\begin{cases}
1/2, & -1<x<1\\
0, & \text{otherwise}
\end{cases}\\
f_y &=
\begin{cases}
1/\sqrt{y}, & 0<x<1\\
0, & \text{otherwise}
\end{cases}
\end{align}

The Attempt at a Solution


$$
cov(X,Y) = E[XY] - E[X]E[Y] = E[XY] - 0\cdot 1/2 = E[XY]
$$
Now
$$
E[XY] = \int_0^1\int_{-1}^1g(X, Y)f_{x,y}(x,y)dxdy
$$
From the information that I have, can I determine ##E[XY]##?
 
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  • #2
I suggest using the fact that ##Y = X^2##...
 
  • #3
Orodruin said:
I suggest using the fact that ##Y = X^2##...

How?
 
  • #4
What do you get if you replace ##Y## everywhere by ##X^2##?
 
  • Like
Likes Dustinsfl
  • #5
Dustinsfl said:
How?

For any observed value ##X = x##, the observed (or, rather, computed) value of ##Y## is ##y = x^2##. It not just that ##Y## and ##X^2## have the same distribution; much more than that is true: ##Y## IS ##X^2##.
 

Related to Expected value of X and Y, E[XY] for uniform random variables

1. What is the formula for calculating the expected value of X and Y, E[XY] for uniform random variables?

The formula for calculating the expected value, or mean, of two uniform random variables X and Y is: E[XY] = (E[X] + E[Y]) / 2. This means that the expected value of the product of X and Y is equal to the average of the expected values of X and Y separately.

2. How is the expected value of X and Y, E[XY] for uniform random variables affected by changes in the distribution parameters?

The expected value of X and Y, E[XY], is not affected by changes in the distribution parameters of the uniform random variables. This is because the expected value is a measure of the central tendency of the distribution, and is not affected by the spread or shape of the distribution.

3. Can the expected value of X and Y, E[XY] for uniform random variables be negative?

No, the expected value of X and Y, E[XY], cannot be negative for uniform random variables. This is because the expected value is a measure of the average outcome, and negative values are not possible for uniform distributions.

4. How does the expected value of X and Y, E[XY] for uniform random variables relate to the variance of X and Y?

The expected value of X and Y, E[XY], is related to the variance of X and Y through the covariance of X and Y. The covariance is a measure of the joint variability of two random variables, and is used to calculate the expected value of the product of X and Y.

5. Can the expected value of X and Y, E[XY] for uniform random variables be greater than the product of the expected values of X and Y, E[X] and E[Y]?

Yes, the expected value of X and Y, E[XY], can be greater than the product of the expected values of X and Y, E[X] and E[Y]. This can occur when there is a positive correlation between X and Y, meaning that when one variable increases, the other variable tends to also increase. In this case, the expected value of the product, E[XY], will be higher than the product of the expected values, E[X]E[Y].

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