Changes

[[File:HeuristicLayerSelectionGraphv3-1.PNG|400px|right]]The within-cluster variance (and so F-stat and variance explained) revealed an issue with the data that had to be fixed: The Python HCA script forces the decomposition of multitons into singletons at the end of its run! We want to stop the HCA when we have every location in a separate point, rather than artificially forcing startups with the same location into separate points. This issue likely doesn't affect the maximum R2 method, but does affect directly affects the heuristic method(s) that rely on layer indicesand indirectly (by changing observation counts) affects the maximum r2 layer choice.

I pushed through the change and reran everything (it took a couple of hours). It is build '''version 3.1'''. The new elbow layer is: 2.5795 x^3 - 3.7445 x^2 + 0.1989 x + 0.9808≈0.492554 at x≈0.483879 [https://www.wolframalpha.com/input/?i=inflection+points+2.5795x3+-+3.7445x2++%2B+0.1989x+%2B+0.9808].

[[File:HeuristicLayerSelectionGraphv3-1.PNG{{Colored box|400pxtitle=NOTICE|right]]content= The new elbow layer is: 2.5795 x^3 - 3.7445 x^2 + 0results in the section below are outdated! The updated results are similar but not the same.1989 x + 0.9808≈0.492554 at x≈0.483879 [httpsDo not rely on the results on the wiki page://www.wolframalpha.com/input/?i=inflection+points+2.5795x3+-+3.7445x2++%2B+0.1989x+%2B+0check the log file for the latest result.9808]}}