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Thank you for this information. I already had a feeling that N coefficients might not suffice. The reason I asked for this was that I wanted to find $\boldsymbol{C}_{\boldsymbol{xy}}$ based on a given degree of fit between $\boldsymbol{x}$ and $\boldsymbol{y}$. Meanwhile, I think I found a way to do it: Based on the relation $$\textrm{Cov}[\boldsymbol{x}-\...


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This question belongs more on stats.SE (where many similar questions have been thrashed out in detail) but nonetheless here goes. Let's take the simplest case of $N=1$. Just because $X$ and $Y$ are Gaussian random variables, it is not necessarily the case that $X$ and $Y$ have a jointly Gaussian distribution. See, for example, this answer on stats.SE for ...


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You would use the complex result which shows the magnitude and phase of the cross correlation (both are important depending on the use of the result). For example, if this was used to determine carrier phase in a receiver where there is an offset in frequency between the transmitted signal and the first estimate of its carrier in the receiver, then by ...


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df.corr() # Compute pairwise correlation of columns, excluding NA/null values. you can see that in pandas documentation it means you do not need the loop plus you can choose the method of cor


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