Does Covariance Remain Unchanged Under Variable Transformations?

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In summary, the correlation between two random variables X1 and Y1 being 0 does not necessarily mean that the correlation between their transformed variables X2 and Y2 will also be 0. This is because the transformation function g can change the relationship between the variables. For example, if X1 and Y1 are uncorrelated but not independent, their transformed variables X2 and Y2 may have a non-zero correlation. This can be seen in the example of random variables X and Y with a specific joint distribution and a transformation function g(w) = w^2, where the correlation between X and Y is 0 but the correlation between X^2 and Y^2 is not 0.
  • #1
PAHV
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Let X1 and Y1 be two random variables. We have Cov(X1,Y1) = 0. Does this extend to any transformation X2 = g(X1) and Y2 = g(Y1), such that Cov(X2,Y2)? Here, g is a continuous function. For example, if we set X2 = X1^2 and Y2 = Y1^2. Do we then from Cov(X1,Y1) = 0 that Cov(X1^2,Y1^2) = 0?
 
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  • #2
No. For example, if X1 and Y1 are uncorrelated but not independent, then your X2 and Y2 may have a non-zero correlation.
 
  • #3
For example, try random variables X and Y with the joint distribution given by:

P(X=-2,Y=3) = 1/4
P(X=-1,Y=2) = 1/4
P(X=1,Y=2) = 1/4
P(X=2,Y=3) = 1/4

and transform by g(w) = w^2
 

1. What is covariance?

Covariance is a statistical measure that describes the relationship between two variables. It measures how much two variables change together, or in other words, how much they vary from their respective means in a similar way.

2. How is covariance calculated?

Covariance is calculated by taking the sum of the products of the differences between each data point and the respective means of the two variables. This sum is then divided by the total number of data points.

3. What does a positive covariance mean?

A positive covariance means that the two variables have a direct relationship, meaning that when one variable increases, the other variable also tends to increase. This indicates a positive correlation between the two variables.

4. What does a negative covariance mean?

A negative covariance means that the two variables have an inverse relationship, meaning that when one variable increases, the other variable tends to decrease. This indicates a negative correlation between the two variables.

5. How is covariance interpreted?

The magnitude of the covariance can be difficult to interpret on its own. To better understand the relationship between the two variables, it is often useful to compare the covariance to the standard deviations of each variable. A larger covariance compared to the standard deviations indicates a stronger relationship between the two variables.

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