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Apply edits from Allen Downey's review of the linear_regression docs. (GH-26176)

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Raymond Hettinger 2021-05-16 19:21:14 -07:00 • committed by GitHub
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Lib/statistics.py
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@ -930,15 +930,15 @@ def linear_regression(regressor, dependent_variable, /):
Return the intercept and slope of simple linear regression
parameters estimated using ordinary least squares. Simple linear
regression describes relationship between *regressor* and
*dependent variable* in terms of linear function::
*dependent variable* in terms of linear function:
dependent_variable = intercept + slope * regressor + noise
where ``intercept`` and ``slope`` are the regression parameters that are
estimated, and noise term is an unobserved random variable, for the
variability of the data that was not explained by the linear regression
(it is equal to the difference between prediction and the actual values
of dependent variable).
where *intercept* and *slope* are the regression parameters that are
estimated, and noise represents the variability of the data that was
not explained by the linear regression (it is equal to the
difference between predicted and actual values of dependent
variable).
The parameters are returned as a named tuple.