bpo-44151: Various grammar, word order, and markup fixes (GH-26344) (GH-26345)
diff --git a/Doc/library/statistics.rst b/Doc/library/statistics.rst
index bf87e41..3c3f9d2 100644
--- a/Doc/library/statistics.rst
+++ b/Doc/library/statistics.rst
@@ -626,7 +626,7 @@
.. versionadded:: 3.10
-.. function:: linear_regression(independent_variable, dependent_variable)
+.. function:: linear_regression(x, y, /)
Return the slope and intercept of `simple linear regression
<https://en.wikipedia.org/wiki/Simple_linear_regression>`_
@@ -634,30 +634,30 @@
regression describes the relationship between an independent variable *x* and
a dependent variable *y* in terms of this linear function:
- *y = intercept + slope \* x + noise*
+ *y = slope \* x + intercept + noise*
where ``slope`` and ``intercept`` are the regression parameters that are
- estimated, and noise represents the
+ 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).
+ of the dependent variable).
Both inputs must be of the same length (no less than two), and
- the independent variable *x* needs not to be constant;
- otherwise :exc:`StatisticsError` is raised.
+ the independent variable *x* cannot be constant;
+ otherwise a :exc:`StatisticsError` is raised.
For example, we can use the `release dates of the Monty
- Python films <https://en.wikipedia.org/wiki/Monty_Python#Films>`_, and used
- it to predict the cumulative number of Monty Python films
+ Python films <https://en.wikipedia.org/wiki/Monty_Python#Films>`_
+ to predict the cumulative number of Monty Python films
that would have been produced by 2019
- assuming that they kept the pace.
+ assuming that they had kept the pace.
.. doctest::
>>> year = [1971, 1975, 1979, 1982, 1983]
>>> films_total = [1, 2, 3, 4, 5]
>>> slope, intercept = linear_regression(year, films_total)
- >>> round(intercept + slope * 2019)
+ >>> round(slope * 2019 + intercept)
16
.. versionadded:: 3.10
diff --git a/Lib/statistics.py b/Lib/statistics.py
index f164210a..b1b6131 100644
--- a/Lib/statistics.py
+++ b/Lib/statistics.py
@@ -923,26 +923,26 @@ def correlation(x, y, /):
def linear_regression(x, y, /):
- """Intercept and slope for simple linear regression
+ """Slope and intercept for simple linear regression.
- Return the intercept and slope of simple linear regression
+ Return the slope and intercept of simple linear regression
parameters estimated using ordinary least squares. Simple linear
- regression describes relationship between *x* and
- *y* in terms of linear function:
+ regression describes relationship between an independent variable
+ *x* and a dependent variable *y* in terms of linear function:
- y = intercept + slope * x + noise
+ y = slope * x + intercept + noise
- where *intercept* and *slope* are the regression parameters that are
+ where *slope* and *intercept* 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
+ difference between predicted and actual values of the dependent
variable).
The parameters are returned as a named tuple.
>>> x = [1, 2, 3, 4, 5]
>>> noise = NormalDist().samples(5, seed=42)
- >>> y = [2 + 3 * x[i] + noise[i] for i in range(5)]
+ >>> y = [3 * x[i] + 2 + noise[i] for i in range(5)]
>>> linear_regression(x, y) #doctest: +ELLIPSIS
LinearRegression(slope=3.09078914170..., intercept=1.75684970486...)