Finding covariance using the joint pdf

In summary: I'm not sure where I went wrong with this one. In summary, the conversation is about finding the covariance for a given function and using two different methods to calculate it. The person asking the question used the alternate definition of covariance but did not get the expected answer. They are looking for assistance in finding their mistake.
  • #1
Flashcop
2
0
The following question appeared on a practice exam:

For
f(x,y) = 24xy if 0<x+y<1 , 0<x,y
0 elsewhere

find Cov(X,Y)

I used Cov(X,Y) = E(XY) - E(X)E(Y) to calculate covariance, with

E(XY) = [itex]\int^{1}_{0}\int^{1-y}_{0}24x^{2}y^{2}dxdy[/itex]

but for some reason I didn't get the suggested answer of 1/15

Can someone explain to me what I did wrong? Was there something wrong with the terminals?
 
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  • #2
Flashcop said:
The following question appeared on a practice exam:

For
f(x,y) = 24xy if 0<x+y<1 , 0<x,y
0 elsewhere

find Cov(X,Y)

I used Cov(X,Y) = E(XY) - E(X)E(Y) to calculate covariance, with

E(XY) = [itex]\int^{1}_{0}\int^{1-y}_{0}24x^{2}y^{2}dxdy[/itex]

but for some reason I didn't get the suggested answer of 1/15

Can someone explain to me what I did wrong? Was there something wrong with the terminals?

Hey Flashcop and welcome to the forums.

If I were you I would in later cases use the alternative definition COV(X,Y) = E[[X-E[X][Y-E[Y]] and calculate (X - E[X])(Y - E[Y])f(x,y)dxdy for the integral to double check your calculations if you think something is awry (it is redundant but it is a good way to cross-check your calculations in the case of say an algebraic mistake).

For this problem, I think the best way to get relevant advice is to go through your calculation step by step and post it here so that we can go through your algebra and your reasoning.
 
  • #3
chiro said:
Hey Flashcop and welcome to the forums.

If I were you I would in later cases use the alternative definition COV(X,Y) = E[[X-E[X][Y-E[Y]] and calculate (X - E[X])(Y - E[Y])f(x,y)dxdy for the integral to double check your calculations if you think something is awry (it is redundant but it is a good way to cross-check your calculations in the case of say an algebraic mistake).

For this problem, I think the best way to get relevant advice is to go through your calculation step by step and post it here so that we can go through your algebra and your reasoning.

Thanks mate :)

So using the alternate definition, I got:

Cov(X,Y) [itex]= 24 \int^{1}_{0}\int^{1-y}_{0}(x-\frac{2}{5})(y-\frac{2}{5})xydxdy[/itex]
[itex]= 24 \int^{1}_{0}\int^{1-y}_{0}x^{2}y^{2}-\frac{2x^{2}y}{5}-\frac{2xy^{2}}{5}+\frac{4xy}{25}dxdy[/itex]
[itex]=24\int^{1}_{0}\frac{-1}{75}(2-5y)^{2}(y-1)^{2}ydy[/itex]
[itex]=\frac{-24}{75}\int^{1}_{0}(2-5y)^{2}(y-1)^{2}ydy[/itex]
[itex]=\frac{-24}{75}*\frac{1}{12}
=\frac{-2}{75} [/itex]

Which is the same answer I got before, so that probably rules out any algebraic errors.
 

Related to Finding covariance using the joint pdf

1. What is the purpose of finding covariance using the joint pdf?

The purpose of finding covariance using the joint pdf is to measure the relationship between two random variables. It helps to determine how much the two variables vary together, and whether they have a positive or negative relationship.

2. Can the joint pdf be used to find covariance for any two random variables?

Yes, the joint pdf can be used to find covariance for any two random variables as long as they have a joint probability distribution function.

3. How is covariance calculated using the joint pdf?

Covariance can be calculated using the joint pdf by taking the expected value of the product of the two random variables minus the product of their individual expected values.

4. What does a positive/negative covariance value indicate?

A positive covariance value indicates that the two variables have a positive relationship, meaning that when one variable increases, the other tends to increase as well. A negative covariance value indicates a negative relationship, where one variable tends to decrease as the other increases.

5. How is covariance different from correlation?

Covariance and correlation both measure the relationship between two variables, but covariance is not standardized and can have varying units of measurement. Correlation, on the other hand, is standardized and always has a value between -1 and 1, making it easier to compare relationships between different variables.

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