Correlation of U=Z+X and V=Z+Y

In summary, X, Y, and Z are uncorrelated random variables with variances \sigma^{2}_{X}, \sigma^{2}_{Y}, and \sigma^{2}_{Z}, respectively. When U=Z+X and V=Z+Y, the correlation coefficient \rho_{UV} can be calculated using the formula \rho_{UV}=\frac{Var(Z)}{\sqrt{[Var(Z)+Var(X)][Var(Z)+Var(Y)]}}. This cannot be simplified further.
  • #1
cborse

Homework Statement



##X##, ##Y## and ##Z## are uncorrelated random variables with variances [itex]\sigma^{2}_{X}[/itex], [itex]\sigma^{2}_{Y}[/itex] and [itex]\sigma^{2}_{Z}[/itex], respectively. ##U=Z+X## and ##V=Z+Y##. Find [itex]\rho_{UV}[/itex].

Homework Equations



[itex]\rho_{UV}=\frac{Cov(U,V)}{\sqrt{Var(U)Var(V)}}[/itex]

The Attempt at a Solution



Since X, Y and Z are uncorrelated, Cov(X,Y)=Cov(X,Z)=Cov(Y,Z)=0. So,

##Cov(U,V)=Cov(Z+X,Z+Y)=Var(Z)=##

The variances are

##Var(U)=Var(Z+X)=Var(Z)+Var(X)##
##Var(V)=Var(Z+Y)=Var(Z)+Var(Y)##

Putting it all together gives

[itex]\rho_{UV}=\frac{Var(Z)}{\sqrt{[Var(Z)+Var(X)][Var(Z)+Var(Y)]}}[/itex]

This seems correct. Could it be simplified further?
 
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  • #2
I don't see how it could be simplified.
 
  • #3
Thanks.
 

1. What is the meaning of correlation in this equation?

The correlation in this equation refers to the relationship between two variables, U and V, and their respective components, Z and X for U and Z and Y for V.

2. How is the correlation calculated in this equation?

The correlation between U and V is calculated by finding the correlation between Z and X, and then adding it to the correlation between Z and Y.

3. Can this equation be used to determine causation?

No, this equation only shows the relationship between the variables and their components. It does not prove causation, which would require further analysis and experimentation.

4. Is this equation applicable to all types of data?

Yes, this equation can be used for both numerical and categorical data. However, the results may vary depending on the type of data and the strength of the correlation.

5. How can this equation be used in practical applications?

This equation can be used in various fields such as social sciences, economics, and data analysis to understand the relationship between variables and their components and make predictions based on the correlation.

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