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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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26
Doc/library/statistics.rst
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@ -631,25 +631,25 @@ However, for reading convenience, most of the examples show sorted sequences.
Return the intercept and slope of `simple linear regression
<https://en.wikipedia.org/wiki/Simple_linear_regression>`_
parameters estimated using ordinary least squares. Simple linear
regression describes relationship between *regressor* and
*dependent variable* in terms of linear function:
regression describes the relationship between *regressor* and
*dependent variable* in terms of this 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
estimated, and noise represents 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
(it is equal to the difference between predicted and actual values
of dependent variable).
Both inputs must be of the same length (no less than two), and regressor
needs not to be constant, otherwise :exc:`StatisticsError` is raised.
needs not to be constant; otherwise :exc:`StatisticsError` is raised.
For example, if we took the data on the data on `release dates of the Monty
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 produced, we could
predict what would be the number of films they could have made till year
2019, assuming that they kept the pace.
it to predict the cumulative number of Monty Python films
that would have been produced by 2019
assuming that they kept the pace.
.. doctest::
@ -659,14 +659,6 @@ However, for reading convenience, most of the examples show sorted sequences.
>>> round(intercept + slope * 2019)
16
We could also use it to "predict" how many Monty Python films existed when
Brian Cohen was born.
.. doctest::
>>> round(intercept + slope * 1)
-610
.. versionadded:: 3.10

12
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.