Calculating covariance from variances

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In summary, we have three independent random variables, X1, X2, and X3, with variances 3, 4, and 8 respectively. Using the general relationship Cov(W+X, Y+Z) = Cov(W,Y) + Cov(W, Z) + Cov(X, Y) + Cov(X, Z), we can find the covariance of Yi and Yj for all i, j. However, since the variables are independent, the only non-zero covariances are those with themselves, which are just the variances multiplied by a constant. Therefore, the covariances for Y1, Y2, and Y3 are 6, -1, and 1, respectively. The means of
  • #1
gajohnson
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Homework Statement



Suppose that X1, X2, and X3
are independent random variables with variances 3, 4, and 8, respectively.
Let Y1 = 2X1 + 3X2, Y2 = X3 – X2, and Y3 = X1 + X2 + X3. (a) Using the general
relationship
Cov(W+X, Y+Z) = Cov(W,Y) + Cov(W, Z) + Cov(X, Y) + Cov(X, Z), find
Cov(Yi, Yj) for all i, j.

Homework Equations





The Attempt at a Solution



I can set up the Cov(Yi, Yj) for all i, j easily enough, but I do not understand how to calculate, say, 2Cov(X1, X3) just from the variances of X1 and X3. I know this is trivial, but any help would be greatly appreciated.
 
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  • #2
gajohnson said:

Homework Statement



Suppose that X1, X2, and X3
are independent random variables with variances 3, 4, and 8, respectively.
Let Y1 = 2X1 + 3X2, Y2 = X3 – X2, and Y3 = X1 + X2 + X3. (a) Using the general
relationship
Cov(W+X, Y+Z) = Cov(W,Y) + Cov(W, Z) + Cov(X, Y) + Cov(X, Z), find
Cov(Yi, Yj) for all i, j.

Homework Equations





The Attempt at a Solution



I can set up the Cov(Yi, Yj) for all i, j easily enough, but I do not understand how to calculate, say, 2Cov(X1, X3) just from the variances of X1 and X3. I know this is trivial, but any help would be greatly appreciated.

What is the covariance of two independent random variables?
 
  • #3
Ray Vickson said:
What is the covariance of two independent random variables?

Oh, boy...I seem to have missed the "independent" condition in the problem. It works out nicely that each combination seems to have a random variable with a covariance of itself somewhere in there. The rest all becomes 0s.

Thanks!
 
  • #4
b
gajohnson said:
Oh, boy...I seem to have missed the "independent" condition in the problem. It works out nicely that each combination seems to have a random variable with a covariance of itself somewhere in there. The rest all becomes 0s.

Thanks!

Also: to compute the covariances you need to know the means of the X_i, which seem to not have been given. Were they given, and you just forgot to include them here?
 
  • #5
Ray Vickson said:
b

Also: to compute the covariances you need to know the means of the X_i, which seem to not have been given. Were they given, and you just forgot to include them here?

They were not given but, thankfully, because they are independent, the only covariances that are not 0 are those that are just a covariance with itself, i.e. the variance already given--usually multiplied by some constant.
 

1. What is covariance?

Covariance is a statistical measure that describes the relationship between two variables. It indicates the extent to which two variables change together.

2. How is covariance calculated?

Covariance can be calculated by multiplying the deviations of each variable from their respective means and then taking the average of those products.

3. Can covariance be negative?

Yes, covariance can be negative, positive, or zero. A positive covariance indicates a direct relationship between two variables, while a negative covariance indicates an inverse relationship.

4. What is the difference between covariance and correlation?

Covariance and correlation both measure the relationship between two variables, but correlation is a standardized measure that ranges from -1 to 1, while covariance is not standardized and can take on any value.

5. What is the significance of calculating covariance from variances?

Calculating covariance from variances can help determine the strength and direction of the relationship between two variables. It can also be used to calculate other important statistical measures, such as the correlation coefficient.

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