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bpo-44151: Various grammar, word order, and markup fixes (GH-26344)
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2 changed files with 18 additions and 18 deletions
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@ -643,7 +643,7 @@ However, for reading convenience, most of the examples show sorted sequences.
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.. versionadded:: 3.10
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.. function:: linear_regression(independent_variable, dependent_variable)
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.. function:: linear_regression(x, y, /)
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Return the slope and intercept of `simple linear regression
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<https://en.wikipedia.org/wiki/Simple_linear_regression>`_
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@ -651,30 +651,30 @@ However, for reading convenience, most of the examples show sorted sequences.
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regression describes the relationship between an independent variable *x* and
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a dependent variable *y* in terms of this linear function:
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*y = intercept + slope \* x + noise*
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*y = slope \* x + intercept + noise*
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where ``slope`` and ``intercept`` are the regression parameters that are
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estimated, and noise represents the
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estimated, and ``noise`` represents the
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variability of the data that was not explained by the linear regression
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(it is equal to the difference between predicted and actual values
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of dependent variable).
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of the dependent variable).
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Both inputs must be of the same length (no less than two), and
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the independent variable *x* needs not to be constant;
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otherwise :exc:`StatisticsError` is raised.
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the independent variable *x* cannot be constant;
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otherwise a :exc:`StatisticsError` is raised.
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For example, we can use the `release dates of the Monty
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Python films <https://en.wikipedia.org/wiki/Monty_Python#Films>`_, and used
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it to predict the cumulative number of Monty Python films
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Python films <https://en.wikipedia.org/wiki/Monty_Python#Films>`_
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to predict the cumulative number of Monty Python films
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that would have been produced by 2019
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assuming that they kept the pace.
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assuming that they had kept the pace.
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.. doctest::
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>>> year = [1971, 1975, 1979, 1982, 1983]
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>>> films_total = [1, 2, 3, 4, 5]
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>>> slope, intercept = linear_regression(year, films_total)
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>>> round(intercept + slope * 2019)
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>>> round(slope * 2019 + intercept)
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16
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.. versionadded:: 3.10
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