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Confidence ellipses

Properties of BVD confidence ellipses – II – dependency of half-axes on the correlation coefficient

If you have read my last post on confidence ellipses, you may have tried to derive the result on the longer half-axis for maximum correlation by following an eigenvalue analysis of the (inverse) covariance matrix of a Bivariate Normal Distribution [BVD]. If you have succeeded, jump over this post. If not, the contents my be interesting for you. Its is… Read More »Properties of BVD confidence ellipses – II – dependency of half-axes on the correlation coefficient

BVD confidence ellipses for varying correlation

Properties of BVD confidence ellipses – I – constant limits and tangents in x- and y-direction during variation of the Pearson correlation coefficient

We have gathered a lot of knowledge about Bivariate Normal Distributions [BVDs] and their contour ellipses in the math section of this blog. We can now analyze some secondary and funny properties of BVD contour and confidence ellipses. Among other things the variation of some key properties with the Pearson correlation coefficient ρ is of interest for data analysts. In… Read More »Properties of BVD confidence ellipses – I – constant limits and tangents in x- and y-direction during variation of the Pearson correlation coefficient

Confidence ellipses based on covariance matrix

Compute confidence ellipses – I – simple method based on the Pearson correlation coefficient

This post was motivated by a publication of Carsten Schelp [1]. Actually, a long time ago. I used his results in 2021, when I had to plot confidence ellipses during the analysis of statistical (multivariate) vector distributions produced a Machine Learning algorithm. So, all acknowledgements belong to Schelp’s work. However, his ideas have also triggered some of my own efforts… Read More »Compute confidence ellipses – I – simple method based on the Pearson correlation coefficient