What is the correlation of X and Z in terms of variance and covariance?

In summary, this conversation discusses the calculation of Cov(X,Z) and the correlation coefficient of X and Z, given the variances of independent random variables X and Y. The value of Cov(X,Z) is determined to be 0, as Cov(X,Y) is 0 due to independence. The correlation coefficient is calculated to be Cov(X,Z)/12. The conversation concludes with a suggestion to write out the definition of Cov to understand why Cov(X,Z) = Cov(X,X-Y).
  • #1
CescGoal
3
0
Very sorry that I've double posted but I realized i placed the original post in Precalculus.



1. Homework Statement
Question

Let X and Y be independent random variables with variances 9 and 7 respectively and let
Z = X - Y

a) What is the value of Cov(X,Z)
b) What is the value of the correlation coefficient of X and Z?

I've been stuck on this one question for 2-3 hours; its ridiculous, I know. Here's my terrible try.


3. The Attempt at a Solution
a)

Var(X) = 9
Var(Y) = 7

Var(X-Y) = Var(X) + Var(Y) = Var(Z)
Therefore, Var(Z) = 7 + 9 =16
Cov(X,Z) = E[XZ] - E[X]E[Z]


and b) [tex]\rho[/tex]XZ = [tex]\frac{Cov(X,Z)}{\sqrt{Var(X)*Var(Z)}}[/tex]



= [tex]\frac{Cov(X,Z)}{\sqrt{9}*\sqrt{16}}[/tex]
= [tex]\frac{Cov(X,Z)}{12}[/tex]

Since last topic, I've realized that Cov(X,Y) = 0 due to independency. But I don't know how to use it.
 
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  • #2


Cov(X,Z)=Cov(X,X-Y). Cov(X,X-Y)=Cov(X,X)-Cov(X,Y), right?
 
  • #3


I didn't know about that law; thankyou very much.
 
  • #4


CescGoal said:
I didn't know about that law; thankyou very much.

It's pretty obvious if you write out the definition of Cov. You should try and do that so you can see why it's true.
 

FAQ: What is the correlation of X and Z in terms of variance and covariance?

What is variance?

Variance is a statistical measure of how spread out a set of data is. It is calculated by taking the average of the squared differences from the mean.

How is variance used in data analysis?

Variance is used to understand the variability of a data set and can help identify patterns and trends. It is also used in hypothesis testing and to determine the accuracy of statistical models.

What is covariance?

Covariance is a measure of the relationship between two variables. It indicates how much two variables change together. A positive covariance means that the two variables tend to increase or decrease together, while a negative covariance means that they tend to move in opposite directions.

How is covariance different from correlation?

Covariance and correlation are both measures of the relationship between two variables, but correlation is a standardized version of covariance. This means that correlation is always between -1 and 1, while covariance can have any value. Correlation is also a more useful measure because it takes into account the scales of the two variables, making it easier to interpret.

What can we learn from the covariance matrix?

The covariance matrix is a table that shows the covariance between multiple variables. It can help identify which variables are strongly related and which are not. It is also used in multivariate analysis to determine which variables have the most influence on a particular outcome.

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