I have several 20,000+ point scatter plots that I'd like to graph with a best fit. Whats the best way to do this?

Since they're so large and occupy the same range of y values, I'm kind of reluctant to plot them one over another so a best fit line would, I think, be the most helpful in analysing the data. I'm not quite sure however how to go about this since the data can be random

Well, here's a very primitive way to decimate the data without any fancy algorithm. For this particular case, it looks pretty good. Each loop makes the array 3x smaller, averaging 3 adjacent elements. 4 iterations seem to be perfect. (I have included your raw data in diagram constants).
For a polynomial fit, the more complex the data, the higher the order polynomial is needed to describe it. If it is a simple "banana" (my example above) you're OK with an order of 2 (up to quadratic), if it is s-shaped, you need at least 3rd order, etc. I tried your data with much higher orders (up to 40!) and it did not work.
LabVIEW Champion . Do more with less code and in less time .
Attachments:
Best_Fit2.vi ‏370 KB

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