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With reference to the original thread on Stackexchange, my question is as follows.

Usually, one would enter two value-series and a script or program calculates the correlation. For instance, with $x = 5,3,6,7,4,2,9,5$ and $y = 4,3,4,8,3,2,10,5$, the correlation is $0.93439982209434$.

For an educational website, I'm trying to find a way to let students:

  • put in value series $x$, eg. $x = 5,3,6,7,4,2,9,5$
  • put in the correlation, eg. $0.9344$
  • put in upper and lower boundaries of $y$-series, eg. between $1$ and $10$
  • give back a random series which fits the citeria, eg. $y = 4,3,4,8,3,2,10,5$

The PHP script I have written to calculate the correlation can be found in the referred-to post on stackexchange. However, it was suggested mine was much more a mathematical than a programmatical question, hence this post. Would it be possible to execute this "reverse correlation"?

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If you restrict to integers between bounds, then there is no guarantee that there is a sequence that gives the exact correlation entered. – huon May 15 '12 at 12:14
But without bounds, the returned sequence matching [1,2,3] could be as large as [10000,20000,30000], for instance. I believe the educational purpose would be somewhat lost there. The boundaries would be there to return a sequence af approximately the same order of magnitude as the given sequence. Would it be feasible to, within bounderies, return an approximation of the correlation given? In other words, if the returned sequence would correlate not to 0.9344 but 0.899, then that would also be acceptable. Perhaps a plus/minus 10% boundary could be set? – Pr0no May 15 '12 at 12:21
do you restrict to integers values? not only that makes your problem more artificial/difficult, but I also consider it potentially misleading from an educational POV – leonbloy May 15 '12 at 13:42
Although I can see how you got that from the OP, I do not restrict to integer values. – Pr0no May 15 '12 at 16:45

I have one idea about this procedure you may have found helpful. In the linked post, the answer advises you to draw samples randomly until you reach the desired correlation. However, I guess it might take some time if you draw these samples independently of the original sequence. Let us use another trick - namely we construct a random sequence from the original one.

Let $X = (X_1,\dots,X_n)$ be a sequence of iid random variables - and you want for a given $\rho$ to construct a sequence $Y = (Y_1,\dots,Y_n)$ of iid random variables which is bounded: $Y_i\in [a,b]$ and $$ \operatorname{cor}(X,Y) \approx \rho. $$ Well, the idea is to put $Y_i = X_i+\beta \xi_i$ where $\xi_i$ is some noise sequence you choose: e.g. $\xi_i = \pm1$. The parameter $\beta$ is needed to reach the desired correlation level: $$ \operatorname{cor}(X,Y) = \frac{\mathrm{Cov}(X,Y)}{\sigma(X)\sigma(Y)} = \rho. $$ We have $\mathrm{Cov}(X,Y) = \mathrm {Cov}(X,X+\beta \xi) = \sigma^2(X)$ if we assume $\xi$ to be independent of $X$. Also: $$ \sigma^2(Y) = \sigma^2(X)+\beta^2\sigma^2(\xi) $$ hence $$ \rho = \frac{\sigma(X)}{\sqrt{\sigma^2(X)+\beta^2\sigma^2(\xi)}}. $$ If you solve for $\beta$, you obtain $$ \beta = \frac{\sigma(X)}{\sigma(\xi)}\sqrt{1 - \frac{1}{\rho^2}}. $$

The algorithm goes like this:

  1. You're given a sequence $X$ and you estimate from it $\hat{\sigma}(X)$.

  2. You choose the distribution of $\xi$ and draw a sample of it.

  3. You put $\displaystyle{\beta = \frac{\hat\sigma(X)}{\sigma(\xi)}\sqrt{1 - \frac{1}{\rho^2}}} $ and construct the process $Y = X+\beta \xi$; here you reach the desired correlation level.

  4. Using the scaling and shift $y = aY+b$ you reach the desired bounds.

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