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bpo-44151: Various grammar, word order, and markup fixes (GH-26344)

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Raymond Hettinger 2021-05-24 23:04:04 -07:00 committed by GitHub
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20
Doc/library/statistics.rst
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@ -643,7 +643,7 @@ However, for reading convenience, most of the examples show sorted sequences.
.. 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>`_
@ -651,30 +651,30 @@ However, for reading convenience, most of the examples show sorted sequences.
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

16
Lib/statistics.py
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@ -936,26 +936,26 @@ LinearRegression = namedtuple('LinearRegression', ('slope', 'intercept'))
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...)