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I have three data vectors $A$, $B$ and $C$ that are all more or less correlated.

What I want is a meaningful definition for a measure of the degree of correlation between those parts of $A$ and $B$ that are not correlated with $C$.

I do apologize if this is not fully understandable. I struggle to come to an easier formulation of the problem. However I can give an example:

Example: Say $A$, $B$, $C$ could all be binary, i.e. only contain the values 0 and 1. I then would want to know the number of positions where $A$ and $B$ are 1 and $C$ is 0.

As a side note: How does one "uncorrelate" variables, i.e. given two variables $A$ and $B$ how to split $A$ into a part that is maximally correlated with B and another that is maximally uncorrelated?

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  • $\begingroup$ I added the least-squares tag to this question because it provides the answer! $\endgroup$ – whuber Oct 23 '13 at 20:02
  • $\begingroup$ Can you elaborate on this short notice? $\endgroup$ – Trilarion Oct 23 '13 at 21:50
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Correlation, according to its usual definition, is the cosine of the angle between vectors. Least squares regression decomposes a vector into a component within the linear span of a given set of vectors and an orthogonal component (the "residuals" or "errors"). Those two components, being at a right angle to each other, have zero correlation. Therefore,

  • A meaningful measure of the "degree of correlation between those parts of $A$ and $B$ that are not correlated with $C$" is the correlation of the residuals of $A$ and $B$ with each other when regressed separately against $C$.

  • The part of $A$ that is maximally correlated with $B$ is the projection of $A$ onto $B$ (the least-squares fit) and the part that is "maximally uncorrelated" is the residual of the regression of $A$ against $B$.


The example in the question suggests that "correlation" may be used there in a less conventional but undefined sense. Note that the residuals of the regressions of $A$ and $B$ on $C$ when all are binary will usually not be binary.

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  • $\begingroup$ Thank you for the answer, I think it solves my problem. According to it, I'll now correlate the components of $A$ and $B$ that are both orthogonal to $C$. $\endgroup$ – Trilarion Oct 29 '13 at 13:06

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