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Rifles, Reloading, Optics, Equipment
Rifles, Bullets, Barrels & Ballistics
LabRadar: Great Data - Terrible Implementation. (partial solution inside)
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<blockquote data-quote="entoptics" data-source="post: 2150878" data-attributes="member: 104268"><p>I thought problem was minimized for awhile after a software update, but that may be because I got a better setup for keeping the unit aimed and steady. I still run everything through my spreadsheet, and see the errors still, though perhaps not as bad after said update? I haven't updated again in quite some time, so it may be better with the latest version.</p><p></p><p>Here's the link to my spreadsheet.</p><p></p><p>[URL unfurl="true"]https://drive.google.com/open?id=1yU7b_2RMISSUq2KEi5E4nqULtMbdg3F2[/URL]</p><p></p><p>My "rules" involve variation from the best fit (highlighted pink cells, >0.15% off best fit), as well as an initial visual culling. I'll remove any that are obviously WAY off the line, then I sort by variation with the macro button, and cull the worst offenders. Doing the initial visual cull can be important. Particularly if there's several outliers at the beginning or ending of tracking, as they can really pull the best fit line off the majority of data, causing the >0.15% metric to falsely flag otherwise good data.</p></blockquote><p></p>
[QUOTE="entoptics, post: 2150878, member: 104268"] I thought problem was minimized for awhile after a software update, but that may be because I got a better setup for keeping the unit aimed and steady. I still run everything through my spreadsheet, and see the errors still, though perhaps not as bad after said update? I haven't updated again in quite some time, so it may be better with the latest version. Here's the link to my spreadsheet. [URL unfurl="true"]https://drive.google.com/open?id=1yU7b_2RMISSUq2KEi5E4nqULtMbdg3F2[/URL] My "rules" involve variation from the best fit (highlighted pink cells, >0.15% off best fit), as well as an initial visual culling. I'll remove any that are obviously WAY off the line, then I sort by variation with the macro button, and cull the worst offenders. Doing the initial visual cull can be important. Particularly if there's several outliers at the beginning or ending of tracking, as they can really pull the best fit line off the majority of data, causing the >0.15% metric to falsely flag otherwise good data. [/QUOTE]
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LabRadar: Great Data - Terrible Implementation. (partial solution inside)
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