vault backup: 2024-01-02 14:18:59
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@ -113,7 +113,7 @@ $$ \sqrt{1-r^2}(\sigma_y) $$
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- On a least squared regression line, the 1 r.m.s error away will contain $2\sigma$ of the data, and it should loosely mirror a normal curve.
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- To approximate the R.M.S error for a scatter diagram, take a high value and a low value for a given $x$ coordinate, and divide by 4, because r.m.s error is within $2\sigma$ of either side of the line.
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- 68% = $2\sigma$, 95% = $4\sigma$
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- RMS can help determine which observations are outliers. Typically if a value is more than *2 r.m.s* away from the prediction estimate, it is considered to be an outlier.
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---
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# Terminology
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| Term | Definition |
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