From e1469468d7d24a6cc414bbac80a480b51d5d85d1 Mon Sep 17 00:00:00 2001 From: zleyyij Date: Tue, 2 Jan 2024 14:18:59 -0700 Subject: [PATCH] vault backup: 2024-01-02 14:18:59 --- education/statistics/Correlation and Regression.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/education/statistics/Correlation and Regression.md b/education/statistics/Correlation and Regression.md index 08fef55..e9f7451 100644 --- a/education/statistics/Correlation and Regression.md +++ b/education/statistics/Correlation and Regression.md @@ -113,7 +113,7 @@ $$ \sqrt{1-r^2}(\sigma_y) $$ - 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. - 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. - 68% = $2\sigma$, 95% = $4\sigma$ - +- 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. --- # Terminology | Term | Definition |