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Standard Error of the Estimate of a Regression Line

by QuantShare, 2228 days ago
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The standard error of the estimate is a measure that tells us how close our values are from the regression line. In other words, it measures the accuracy of predictions. The lower the standard error of the estimate the better the predictions are.
When applied to the close series, we can measure how narrow the security trending channel is. This measure is also very useful when comparing regression lines of different securities or regression lines of different periods.

Example:

a = stdErrorEstimate(close, 50) > 1.5 * stdErrorEstimate(close, 25);

In the above example, I have calculated the standard error of the estimate of the 50-bar regression line and compared it to the standard error of the estimate of the shorter term regression line.
If the former estimate is 1.5 higher than the latter then returns 1. In other words, the above formula returns true when the short term trending channel becomes narrower than longer term trending channel.
To compare the standard error of estimation of two securities, you can use the "GetSeries" function.
Example:
A = stdErrorEstimate(close, 50) > stdErrorEstimate(GetSeries("GOOG", close), 50);

This will compare the error estimation of the analyzed security to the error estimation of Google.



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