Is it possible for me to use a Voronoi diagram - or some other algorithm - to represent a point in an multidimensional space (between 5 and 10 dimensions) in a 2D geometrical shape? (And more interestingly, in a geometrical shape that is a) fairly small, and b) looks aesthetic.(adsbygoogle = window.adsbygoogle || []).push({});

I have a number of measurements in standard deviation units on different normal distributions. Say I have 8 standard deviation measurements on 8 different variables. This gives me the following numbers (one for each normal distribution):

1: -0.3

2: 1.2

3: 0.7

4: 2.1

5: 0.2

6: -1.3

7: -0.2

8: 1.9

Can I represent these 8 SD measures as a 2D geometrical shape (to encode them) in such a way that the 2D shapes distinguish between e.g. 10ths of a standard deviation unit (to a level of resolution as in this list)? What are my options here?

Thanks... Alan

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# Representing a point in an n-dimensional space in a 2D geometrical shape

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