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This works and is very simple. This is a qualitative way to remove outliers or bad data through chart selection.
For background on my purpose, some data outliers are legitimate points, so using statistical methods yield improper results within final calculation. By graphing xy and visually inspecting on a macro scale (>15000 data) where suspect data resides, points are visually located rapidly within the chart. Any option could work on the suspect points from here. Inherent data gaps created (through clearing) within the range provide quick option to parse whole sections (based on threshold criteria) or fill series (interpolated).
One can change the axis variable easily, but the variable I needed to indicate was the independent. The code below goes within a class module. For detailed scripting on the procedure, look up J. Peltier's information on chart events. This is also coded for chart sheets, not embedded charts. It should also be noted, that once data is deleted, it is permanent, so keep that in mind.
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