Apply edits from Allen Downey's review of the linear_regression docs. (GH-26176) (GH-26185)
diff --git a/Lib/statistics.py b/Lib/statistics.py
index db8c581..c2f8dcd 100644
--- a/Lib/statistics.py
+++ b/Lib/statistics.py
@@ -928,15 +928,15 @@ 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.