forked from AFPy/python-docs-fr
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30 KiB
Plaintext
911 lines
30 KiB
Plaintext
# Copyright (C) 2001-2018, Python Software Foundation
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# For licence information, see README file.
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#
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msgid ""
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msgstr ""
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"Project-Id-Version: Python 3.6\n"
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"Report-Msgid-Bugs-To: \n"
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"POT-Creation-Date: 2019-10-09 17:54+0200\n"
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"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
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"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
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"Language-Team: FRENCH <traductions@lists.afpy.org>\n"
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"Language: fr\n"
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"MIME-Version: 1.0\n"
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"Content-Type: text/plain; charset=UTF-8\n"
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"Content-Transfer-Encoding: 8bit\n"
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#: ../Doc/library/statistics.rst:2
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msgid ":mod:`statistics` --- Mathematical statistics functions"
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msgstr ""
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#: ../Doc/library/statistics.rst:12
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msgid "**Source code:** :source:`Lib/statistics.py`"
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msgstr "**Code source :** :source:`Lib/statistics.py`"
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#: ../Doc/library/statistics.rst:21
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msgid ""
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"This module provides functions for calculating mathematical statistics of "
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"numeric (:class:`~numbers.Real`-valued) data."
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msgstr ""
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#: ../Doc/library/statistics.rst:24
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msgid ""
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"The module is not intended to be a competitor to third-party libraries such "
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"as `NumPy <https://numpy.org>`_, `SciPy <https://www.scipy.org/>`_, or "
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"proprietary full-featured statistics packages aimed at professional "
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"statisticians such as Minitab, SAS and Matlab. It is aimed at the level of "
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"graphing and scientific calculators."
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msgstr ""
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#: ../Doc/library/statistics.rst:30
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msgid ""
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"Unless explicitly noted, these functions support :class:`int`, :class:"
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"`float`, :class:`~decimal.Decimal` and :class:`~fractions.Fraction`. "
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"Behaviour with other types (whether in the numeric tower or not) is "
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"currently unsupported. Collections with a mix of types are also undefined "
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"and implementation-dependent. If your input data consists of mixed types, "
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"you may be able to use :func:`map` to ensure a consistent result, for "
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"example: ``map(float, input_data)``."
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msgstr ""
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#: ../Doc/library/statistics.rst:39
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msgid "Averages and measures of central location"
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msgstr ""
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#: ../Doc/library/statistics.rst:41
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msgid ""
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"These functions calculate an average or typical value from a population or "
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"sample."
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msgstr ""
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#: ../Doc/library/statistics.rst:45
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msgid ":func:`mean`"
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msgstr ""
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#: ../Doc/library/statistics.rst:45
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msgid "Arithmetic mean (\"average\") of data."
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msgstr ""
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#: ../Doc/library/statistics.rst:46
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msgid ":func:`fmean`"
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msgstr ""
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#: ../Doc/library/statistics.rst:46
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msgid "Fast, floating point arithmetic mean."
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msgstr ""
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#: ../Doc/library/statistics.rst:47
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msgid ":func:`geometric_mean`"
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msgstr ""
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#: ../Doc/library/statistics.rst:47
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msgid "Geometric mean of data."
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msgstr ""
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#: ../Doc/library/statistics.rst:48
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msgid ":func:`harmonic_mean`"
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msgstr ""
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#: ../Doc/library/statistics.rst:48
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msgid "Harmonic mean of data."
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msgstr ""
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#: ../Doc/library/statistics.rst:49
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msgid ":func:`median`"
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msgstr ""
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#: ../Doc/library/statistics.rst:49
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msgid "Median (middle value) of data."
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msgstr ""
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#: ../Doc/library/statistics.rst:50
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msgid ":func:`median_low`"
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msgstr ""
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#: ../Doc/library/statistics.rst:50
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msgid "Low median of data."
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msgstr ""
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#: ../Doc/library/statistics.rst:51
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msgid ":func:`median_high`"
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msgstr ""
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#: ../Doc/library/statistics.rst:51
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msgid "High median of data."
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msgstr ""
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#: ../Doc/library/statistics.rst:52
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msgid ":func:`median_grouped`"
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msgstr ""
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#: ../Doc/library/statistics.rst:52
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msgid "Median, or 50th percentile, of grouped data."
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msgstr ""
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#: ../Doc/library/statistics.rst:53
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msgid ":func:`mode`"
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msgstr ""
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#: ../Doc/library/statistics.rst:53
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msgid "Single mode (most common value) of discrete or nominal data."
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msgstr ""
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#: ../Doc/library/statistics.rst:54
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msgid ":func:`multimode`"
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msgstr ""
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#: ../Doc/library/statistics.rst:54
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msgid "List of modes (most common values) of discrete or nomimal data."
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msgstr ""
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#: ../Doc/library/statistics.rst:55
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msgid ":func:`quantiles`"
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msgstr ""
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#: ../Doc/library/statistics.rst:55
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msgid "Divide data into intervals with equal probability."
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msgstr ""
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#: ../Doc/library/statistics.rst:59
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msgid "Measures of spread"
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msgstr ""
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#: ../Doc/library/statistics.rst:61
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msgid ""
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"These functions calculate a measure of how much the population or sample "
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"tends to deviate from the typical or average values."
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msgstr ""
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#: ../Doc/library/statistics.rst:65
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msgid ":func:`pstdev`"
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msgstr ""
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#: ../Doc/library/statistics.rst:65
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msgid "Population standard deviation of data."
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msgstr ""
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#: ../Doc/library/statistics.rst:66
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msgid ":func:`pvariance`"
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msgstr ""
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#: ../Doc/library/statistics.rst:66
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msgid "Population variance of data."
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msgstr ""
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#: ../Doc/library/statistics.rst:67
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msgid ":func:`stdev`"
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msgstr ""
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#: ../Doc/library/statistics.rst:67
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msgid "Sample standard deviation of data."
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msgstr ""
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#: ../Doc/library/statistics.rst:68
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msgid ":func:`variance`"
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msgstr ""
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#: ../Doc/library/statistics.rst:68
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msgid "Sample variance of data."
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msgstr ""
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#: ../Doc/library/statistics.rst:73
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msgid "Function details"
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msgstr ""
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#: ../Doc/library/statistics.rst:75
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msgid ""
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"Note: The functions do not require the data given to them to be sorted. "
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"However, for reading convenience, most of the examples show sorted sequences."
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msgstr ""
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#: ../Doc/library/statistics.rst:80
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msgid ""
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"Return the sample arithmetic mean of *data* which can be a sequence or "
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"iterator."
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msgstr ""
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#: ../Doc/library/statistics.rst:82
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msgid ""
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"The arithmetic mean is the sum of the data divided by the number of data "
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"points. It is commonly called \"the average\", although it is only one of "
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"many different mathematical averages. It is a measure of the central "
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"location of the data."
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msgstr ""
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#: ../Doc/library/statistics.rst:87
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msgid "If *data* is empty, :exc:`StatisticsError` will be raised."
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msgstr ""
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#: ../Doc/library/statistics.rst:89
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msgid "Some examples of use:"
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msgstr ""
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#: ../Doc/library/statistics.rst:108
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msgid ""
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"The mean is strongly affected by outliers and is not a robust estimator for "
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"central location: the mean is not necessarily a typical example of the data "
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"points. For more robust measures of central location, see :func:`median` "
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"and :func:`mode`."
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msgstr ""
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#: ../Doc/library/statistics.rst:113
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msgid ""
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"The sample mean gives an unbiased estimate of the true population mean, so "
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"that when taken on average over all the possible samples, ``mean(sample)`` "
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"converges on the true mean of the entire population. If *data* represents "
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"the entire population rather than a sample, then ``mean(data)`` is "
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"equivalent to calculating the true population mean μ."
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msgstr ""
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#: ../Doc/library/statistics.rst:122
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msgid "Convert *data* to floats and compute the arithmetic mean."
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msgstr ""
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#: ../Doc/library/statistics.rst:124
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msgid ""
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"This runs faster than the :func:`mean` function and it always returns a :"
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"class:`float`. The *data* may be a sequence or iterator. If the input "
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"dataset is empty, raises a :exc:`StatisticsError`."
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msgstr ""
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#: ../Doc/library/statistics.rst:138
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msgid "Convert *data* to floats and compute the geometric mean."
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msgstr ""
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#: ../Doc/library/statistics.rst:140
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msgid ""
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"The geometric mean indicates the central tendency or typical value of the "
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"*data* using the product of the values (as opposed to the arithmetic mean "
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"which uses their sum)."
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msgstr ""
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#: ../Doc/library/statistics.rst:144
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msgid ""
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"Raises a :exc:`StatisticsError` if the input dataset is empty, if it "
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"contains a zero, or if it contains a negative value. The *data* may be a "
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"sequence or iterator."
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msgstr ""
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#: ../Doc/library/statistics.rst:148
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msgid ""
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"No special efforts are made to achieve exact results. (However, this may "
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"change in the future.)"
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msgstr ""
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#: ../Doc/library/statistics.rst:161
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msgid ""
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"Return the harmonic mean of *data*, a sequence or iterator of real-valued "
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"numbers."
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msgstr ""
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#: ../Doc/library/statistics.rst:164
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msgid ""
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"The harmonic mean, sometimes called the subcontrary mean, is the reciprocal "
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"of the arithmetic :func:`mean` of the reciprocals of the data. For example, "
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"the harmonic mean of three values *a*, *b* and *c* will be equivalent to ``3/"
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"(1/a + 1/b + 1/c)``."
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msgstr ""
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#: ../Doc/library/statistics.rst:169
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msgid ""
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"The harmonic mean is a type of average, a measure of the central location of "
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"the data. It is often appropriate when averaging rates or ratios, for "
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"example speeds."
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msgstr ""
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#: ../Doc/library/statistics.rst:173
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msgid ""
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"Suppose a car travels 10 km at 40 km/hr, then another 10 km at 60 km/hr. "
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"What is the average speed?"
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msgstr ""
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#: ../Doc/library/statistics.rst:181
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msgid ""
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"Suppose an investor purchases an equal value of shares in each of three "
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"companies, with P/E (price/earning) ratios of 2.5, 3 and 10. What is the "
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"average P/E ratio for the investor's portfolio?"
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msgstr ""
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#: ../Doc/library/statistics.rst:190
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msgid ""
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":exc:`StatisticsError` is raised if *data* is empty, or any element is less "
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"than zero."
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msgstr ""
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#: ../Doc/library/statistics.rst:198
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msgid ""
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"Return the median (middle value) of numeric data, using the common \"mean of "
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"middle two\" method. If *data* is empty, :exc:`StatisticsError` is raised. "
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"*data* can be a sequence or iterator."
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msgstr ""
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#: ../Doc/library/statistics.rst:202
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msgid ""
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"The median is a robust measure of central location and is less affected by "
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"the presence of outliers. When the number of data points is odd, the middle "
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"data point is returned:"
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msgstr ""
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#: ../Doc/library/statistics.rst:211
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msgid ""
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"When the number of data points is even, the median is interpolated by taking "
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"the average of the two middle values:"
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msgstr ""
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#: ../Doc/library/statistics.rst:219
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msgid ""
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"This is suited for when your data is discrete, and you don't mind that the "
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"median may not be an actual data point."
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msgstr ""
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#: ../Doc/library/statistics.rst:222
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msgid ""
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"If the data is ordinal (supports order operations) but not numeric (doesn't "
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"support addition), consider using :func:`median_low` or :func:`median_high` "
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"instead."
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msgstr ""
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#: ../Doc/library/statistics.rst:228
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msgid ""
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"Return the low median of numeric data. If *data* is empty, :exc:"
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"`StatisticsError` is raised. *data* can be a sequence or iterator."
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msgstr ""
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#: ../Doc/library/statistics.rst:231
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msgid ""
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"The low median is always a member of the data set. When the number of data "
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"points is odd, the middle value is returned. When it is even, the smaller "
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"of the two middle values is returned."
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msgstr ""
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#: ../Doc/library/statistics.rst:242
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msgid ""
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"Use the low median when your data are discrete and you prefer the median to "
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"be an actual data point rather than interpolated."
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msgstr ""
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#: ../Doc/library/statistics.rst:248
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msgid ""
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"Return the high median of data. If *data* is empty, :exc:`StatisticsError` "
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"is raised. *data* can be a sequence or iterator."
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msgstr ""
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#: ../Doc/library/statistics.rst:251
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msgid ""
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"The high median is always a member of the data set. When the number of data "
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"points is odd, the middle value is returned. When it is even, the larger of "
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"the two middle values is returned."
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msgstr ""
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#: ../Doc/library/statistics.rst:262
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msgid ""
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"Use the high median when your data are discrete and you prefer the median to "
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"be an actual data point rather than interpolated."
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msgstr ""
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#: ../Doc/library/statistics.rst:268
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msgid ""
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"Return the median of grouped continuous data, calculated as the 50th "
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"percentile, using interpolation. If *data* is empty, :exc:`StatisticsError` "
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"is raised. *data* can be a sequence or iterator."
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msgstr ""
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#: ../Doc/library/statistics.rst:277
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msgid ""
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"In the following example, the data are rounded, so that each value "
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"represents the midpoint of data classes, e.g. 1 is the midpoint of the class "
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"0.5--1.5, 2 is the midpoint of 1.5--2.5, 3 is the midpoint of 2.5--3.5, "
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"etc. With the data given, the middle value falls somewhere in the class "
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"3.5--4.5, and interpolation is used to estimate it:"
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msgstr ""
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#: ../Doc/library/statistics.rst:288
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msgid ""
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"Optional argument *interval* represents the class interval, and defaults to "
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"1. Changing the class interval naturally will change the interpolation:"
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msgstr ""
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#: ../Doc/library/statistics.rst:298
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msgid ""
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"This function does not check whether the data points are at least *interval* "
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"apart."
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msgstr ""
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#: ../Doc/library/statistics.rst:303
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msgid ""
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"Under some circumstances, :func:`median_grouped` may coerce data points to "
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"floats. This behaviour is likely to change in the future."
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msgstr ""
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#: ../Doc/library/statistics.rst:308
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msgid ""
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"\"Statistics for the Behavioral Sciences\", Frederick J Gravetter and Larry "
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"B Wallnau (8th Edition)."
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msgstr ""
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#: ../Doc/library/statistics.rst:311
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msgid ""
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"The `SSMEDIAN <https://help.gnome.org/users/gnumeric/stable/gnumeric."
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"html#gnumeric-function-SSMEDIAN>`_ function in the Gnome Gnumeric "
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"spreadsheet, including `this discussion <https://mail.gnome.org/archives/"
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"gnumeric-list/2011-April/msg00018.html>`_."
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msgstr ""
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#: ../Doc/library/statistics.rst:319
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msgid ""
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"Return the single most common data point from discrete or nominal *data*. "
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"The mode (when it exists) is the most typical value and serves as a measure "
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"of central location."
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msgstr ""
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#: ../Doc/library/statistics.rst:323
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msgid ""
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"If there are multiple modes with the same frequency, returns the first one "
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"encountered in the *data*. If the smallest or largest of those is desired "
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"instead, use ``min(multimode(data))`` or ``max(multimode(data))``. If the "
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"input *data* is empty, :exc:`StatisticsError` is raised."
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msgstr ""
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#: ../Doc/library/statistics.rst:328
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msgid ""
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"``mode`` assumes discrete data and returns a single value. This is the "
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"standard treatment of the mode as commonly taught in schools:"
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msgstr ""
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#: ../Doc/library/statistics.rst:336
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msgid ""
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"The mode is unique in that it is the only statistic in this package that "
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"also applies to nominal (non-numeric) data:"
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msgstr ""
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#: ../Doc/library/statistics.rst:344
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msgid ""
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"Now handles multimodal datasets by returning the first mode encountered. "
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"Formerly, it raised :exc:`StatisticsError` when more than one mode was found."
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msgstr ""
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#: ../Doc/library/statistics.rst:352
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msgid ""
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"Return a list of the most frequently occurring values in the order they were "
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"first encountered in the *data*. Will return more than one result if there "
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"are multiple modes or an empty list if the *data* is empty:"
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msgstr ""
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#: ../Doc/library/statistics.rst:368
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msgid ""
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"Return the population standard deviation (the square root of the population "
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"variance). See :func:`pvariance` for arguments and other details."
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msgstr ""
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#: ../Doc/library/statistics.rst:379
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msgid ""
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"Return the population variance of *data*, a non-empty sequence or iterator "
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"of real-valued numbers. Variance, or second moment about the mean, is a "
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"measure of the variability (spread or dispersion) of data. A large variance "
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"indicates that the data is spread out; a small variance indicates it is "
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"clustered closely around the mean."
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msgstr ""
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#: ../Doc/library/statistics.rst:385
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msgid ""
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"If the optional second argument *mu* is given, it is typically the mean of "
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"the *data*. It can also be used to compute the second moment around a point "
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"that is not the mean. If it is missing or ``None`` (the default), the "
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"arithmetic mean is automatically calculated."
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msgstr ""
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#: ../Doc/library/statistics.rst:390
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msgid ""
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"Use this function to calculate the variance from the entire population. To "
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"estimate the variance from a sample, the :func:`variance` function is "
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"usually a better choice."
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msgstr ""
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#: ../Doc/library/statistics.rst:394
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msgid "Raises :exc:`StatisticsError` if *data* is empty."
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msgstr ""
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#: ../Doc/library/statistics.rst:396 ../Doc/library/statistics.rst:466
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msgid "Examples:"
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msgstr "Exemples :"
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#: ../Doc/library/statistics.rst:404
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msgid ""
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"If you have already calculated the mean of your data, you can pass it as the "
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"optional second argument *mu* to avoid recalculation:"
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msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:413
|
||
msgid "Decimals and Fractions are supported:"
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:427
|
||
msgid ""
|
||
"When called with the entire population, this gives the population variance "
|
||
"σ². When called on a sample instead, this is the biased sample variance s², "
|
||
"also known as variance with N degrees of freedom."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:431
|
||
msgid ""
|
||
"If you somehow know the true population mean μ, you may use this function to "
|
||
"calculate the variance of a sample, giving the known population mean as the "
|
||
"second argument. Provided the data points are a random sample of the "
|
||
"population, the result will be an unbiased estimate of the population "
|
||
"variance."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:440
|
||
msgid ""
|
||
"Return the sample standard deviation (the square root of the sample "
|
||
"variance). See :func:`variance` for arguments and other details."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:451
|
||
msgid ""
|
||
"Return the sample variance of *data*, an iterable of at least two real-"
|
||
"valued numbers. Variance, or second moment about the mean, is a measure of "
|
||
"the variability (spread or dispersion) of data. A large variance indicates "
|
||
"that the data is spread out; a small variance indicates it is clustered "
|
||
"closely around the mean."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:457
|
||
msgid ""
|
||
"If the optional second argument *xbar* is given, it should be the mean of "
|
||
"*data*. If it is missing or ``None`` (the default), the mean is "
|
||
"automatically calculated."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:461
|
||
msgid ""
|
||
"Use this function when your data is a sample from a population. To calculate "
|
||
"the variance from the entire population, see :func:`pvariance`."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:464
|
||
msgid "Raises :exc:`StatisticsError` if *data* has fewer than two values."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:474
|
||
msgid ""
|
||
"If you have already calculated the mean of your data, you can pass it as the "
|
||
"optional second argument *xbar* to avoid recalculation:"
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:483
|
||
msgid ""
|
||
"This function does not attempt to verify that you have passed the actual "
|
||
"mean as *xbar*. Using arbitrary values for *xbar* can lead to invalid or "
|
||
"impossible results."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:487
|
||
msgid "Decimal and Fraction values are supported:"
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:501
|
||
msgid ""
|
||
"This is the sample variance s² with Bessel's correction, also known as "
|
||
"variance with N-1 degrees of freedom. Provided that the data points are "
|
||
"representative (e.g. independent and identically distributed), the result "
|
||
"should be an unbiased estimate of the true population variance."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:506
|
||
msgid ""
|
||
"If you somehow know the actual population mean μ you should pass it to the :"
|
||
"func:`pvariance` function as the *mu* parameter to get the variance of a "
|
||
"sample."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:512
|
||
msgid ""
|
||
"Divide *data* into *n* continuous intervals with equal probability. Returns "
|
||
"a list of ``n - 1`` cut points separating the intervals."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:515
|
||
msgid ""
|
||
"Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles. Set "
|
||
"*n* to 100 for percentiles which gives the 99 cuts points that separate "
|
||
"*data* into 100 equal sized groups. Raises :exc:`StatisticsError` if *n* is "
|
||
"not least 1."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:520
|
||
msgid ""
|
||
"The *data* can be any iterable containing sample data. For meaningful "
|
||
"results, the number of data points in *data* should be larger than *n*. "
|
||
"Raises :exc:`StatisticsError` if there are not at least two data points."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:524
|
||
msgid ""
|
||
"The cut points are linearly interpolated from the two nearest data points. "
|
||
"For example, if a cut point falls one-third of the distance between two "
|
||
"sample values, ``100`` and ``112``, the cut-point will evaluate to ``104``."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:529
|
||
msgid ""
|
||
"The *method* for computing quantiles can be varied depending on whether the "
|
||
"*data* includes or excludes the lowest and highest possible values from the "
|
||
"population."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:533
|
||
msgid ""
|
||
"The default *method* is \"exclusive\" and is used for data sampled from a "
|
||
"population that can have more extreme values than found in the samples. The "
|
||
"portion of the population falling below the *i-th* of *m* sorted data points "
|
||
"is computed as ``i / (m + 1)``. Given nine sample values, the method sorts "
|
||
"them and assigns the following percentiles: 10%, 20%, 30%, 40%, 50%, 60%, "
|
||
"70%, 80%, 90%."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:540
|
||
msgid ""
|
||
"Setting the *method* to \"inclusive\" is used for describing population data "
|
||
"or for samples that are known to include the most extreme values from the "
|
||
"population. The minimum value in *data* is treated as the 0th percentile "
|
||
"and the maximum value is treated as the 100th percentile. The portion of the "
|
||
"population falling below the *i-th* of *m* sorted data points is computed as "
|
||
"``(i - 1) / (m - 1)``. Given 11 sample values, the method sorts them and "
|
||
"assigns the following percentiles: 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, "
|
||
"80%, 90%, 100%."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:564
|
||
msgid "Exceptions"
|
||
msgstr "Exceptions"
|
||
|
||
#: ../Doc/library/statistics.rst:566
|
||
msgid "A single exception is defined:"
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:570
|
||
msgid "Subclass of :exc:`ValueError` for statistics-related exceptions."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:574
|
||
msgid ":class:`NormalDist` objects"
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:576
|
||
msgid ""
|
||
":class:`NormalDist` is a tool for creating and manipulating normal "
|
||
"distributions of a `random variable <http://www.stat.yale.edu/"
|
||
"Courses/1997-98/101/ranvar.htm>`_. It is a class that treats the mean and "
|
||
"standard deviation of data measurements as a single entity."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:582
|
||
msgid ""
|
||
"Normal distributions arise from the `Central Limit Theorem <https://en."
|
||
"wikipedia.org/wiki/Central_limit_theorem>`_ and have a wide range of "
|
||
"applications in statistics."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:588
|
||
msgid ""
|
||
"Returns a new *NormalDist* object where *mu* represents the `arithmetic mean "
|
||
"<https://en.wikipedia.org/wiki/Arithmetic_mean>`_ and *sigma* represents the "
|
||
"`standard deviation <https://en.wikipedia.org/wiki/Standard_deviation>`_."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:593
|
||
msgid "If *sigma* is negative, raises :exc:`StatisticsError`."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:597
|
||
msgid ""
|
||
"A read-only property for the `arithmetic mean <https://en.wikipedia.org/wiki/"
|
||
"Arithmetic_mean>`_ of a normal distribution."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:603
|
||
msgid ""
|
||
"A read-only property for the `median <https://en.wikipedia.org/wiki/"
|
||
"Median>`_ of a normal distribution."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:609
|
||
msgid ""
|
||
"A read-only property for the `mode <https://en.wikipedia.org/wiki/"
|
||
"Mode_(statistics)>`_ of a normal distribution."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:615
|
||
msgid ""
|
||
"A read-only property for the `standard deviation <https://en.wikipedia.org/"
|
||
"wiki/Standard_deviation>`_ of a normal distribution."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:621
|
||
msgid ""
|
||
"A read-only property for the `variance <https://en.wikipedia.org/wiki/"
|
||
"Variance>`_ of a normal distribution. Equal to the square of the standard "
|
||
"deviation."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:627
|
||
msgid ""
|
||
"Makes a normal distribution instance with *mu* and *sigma* parameters "
|
||
"estimated from the *data* using :func:`fmean` and :func:`stdev`."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:630
|
||
msgid ""
|
||
"The *data* can be any :term:`iterable` and should consist of values that can "
|
||
"be converted to type :class:`float`. If *data* does not contain at least "
|
||
"two elements, raises :exc:`StatisticsError` because it takes at least one "
|
||
"point to estimate a central value and at least two points to estimate "
|
||
"dispersion."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:638
|
||
msgid ""
|
||
"Generates *n* random samples for a given mean and standard deviation. "
|
||
"Returns a :class:`list` of :class:`float` values."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:641
|
||
msgid ""
|
||
"If *seed* is given, creates a new instance of the underlying random number "
|
||
"generator. This is useful for creating reproducible results, even in a "
|
||
"multi-threading context."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:647
|
||
msgid ""
|
||
"Using a `probability density function (pdf) <https://en.wikipedia.org/wiki/"
|
||
"Probability_density_function>`_, compute the relative likelihood that a "
|
||
"random variable *X* will be near the given value *x*. Mathematically, it is "
|
||
"the limit of the ratio ``P(x <= X < x+dx) / dx`` as *dx* approaches zero."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:653
|
||
msgid ""
|
||
"The relative likelihood is computed as the probability of a sample occurring "
|
||
"in a narrow range divided by the width of the range (hence the word \"density"
|
||
"\"). Since the likelihood is relative to other points, its value can be "
|
||
"greater than `1.0`."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:660
|
||
msgid ""
|
||
"Using a `cumulative distribution function (cdf) <https://en.wikipedia.org/"
|
||
"wiki/Cumulative_distribution_function>`_, compute the probability that a "
|
||
"random variable *X* will be less than or equal to *x*. Mathematically, it "
|
||
"is written ``P(X <= x)``."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:667
|
||
msgid ""
|
||
"Compute the inverse cumulative distribution function, also known as the "
|
||
"`quantile function <https://en.wikipedia.org/wiki/Quantile_function>`_ or "
|
||
"the `percent-point <https://www.statisticshowto.datasciencecentral.com/"
|
||
"inverse-distribution-function/>`_ function. Mathematically, it is written "
|
||
"``x : P(X <= x) = p``."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:673
|
||
msgid ""
|
||
"Finds the value *x* of the random variable *X* such that the probability of "
|
||
"the variable being less than or equal to that value equals the given "
|
||
"probability *p*."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:679
|
||
msgid ""
|
||
"Measures the agreement between two normal probability distributions. Returns "
|
||
"a value between 0.0 and 1.0 giving `the overlapping area for the two "
|
||
"probability density functions <https://www.rasch.org/rmt/rmt101r.htm>`_."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:686
|
||
msgid ""
|
||
"Divide the normal distribution into *n* continuous intervals with equal "
|
||
"probability. Returns a list of (n - 1) cut points separating the intervals."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:690
|
||
msgid ""
|
||
"Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles. Set "
|
||
"*n* to 100 for percentiles which gives the 99 cuts points that separate the "
|
||
"normal distribution into 100 equal sized groups."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:694
|
||
msgid ""
|
||
"Instances of :class:`NormalDist` support addition, subtraction, "
|
||
"multiplication and division by a constant. These operations are used for "
|
||
"translation and scaling. For example:"
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:704
|
||
msgid ""
|
||
"Dividing a constant by an instance of :class:`NormalDist` is not supported "
|
||
"because the result wouldn't be normally distributed."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:707
|
||
msgid ""
|
||
"Since normal distributions arise from additive effects of independent "
|
||
"variables, it is possible to `add and subtract two independent normally "
|
||
"distributed random variables <https://en.wikipedia.org/wiki/"
|
||
"Sum_of_normally_distributed_random_variables>`_ represented as instances of :"
|
||
"class:`NormalDist`. For example:"
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:727
|
||
msgid ":class:`NormalDist` Examples and Recipes"
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:729
|
||
msgid ":class:`NormalDist` readily solves classic probability problems."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:731
|
||
msgid ""
|
||
"For example, given `historical data for SAT exams <https://blog.prepscholar."
|
||
"com/sat-standard-deviation>`_ showing that scores are normally distributed "
|
||
"with a mean of 1060 and a standard deviation of 192, determine the "
|
||
"percentage of students with test scores between 1100 and 1200, after "
|
||
"rounding to the nearest whole number:"
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:744
|
||
msgid ""
|
||
"Find the `quartiles <https://en.wikipedia.org/wiki/Quartile>`_ and `deciles "
|
||
"<https://en.wikipedia.org/wiki/Decile>`_ for the SAT scores:"
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:754
|
||
msgid ""
|
||
"To estimate the distribution for a model than isn't easy to solve "
|
||
"analytically, :class:`NormalDist` can generate input samples for a `Monte "
|
||
"Carlo simulation <https://en.wikipedia.org/wiki/Monte_Carlo_method>`_:"
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:770
|
||
msgid "Normal distributions commonly arise in machine learning problems."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:772
|
||
msgid ""
|
||
"Wikipedia has a `nice example of a Naive Bayesian Classifier <https://en."
|
||
"wikipedia.org/wiki/Naive_Bayes_classifier#Sex_classification>`_. The "
|
||
"challenge is to predict a person's gender from measurements of normally "
|
||
"distributed features including height, weight, and foot size."
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:777
|
||
msgid ""
|
||
"We're given a training dataset with measurements for eight people. The "
|
||
"measurements are assumed to be normally distributed, so we summarize the "
|
||
"data with :class:`NormalDist`:"
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:790
|
||
msgid ""
|
||
"Next, we encounter a new person whose feature measurements are known but "
|
||
"whose gender is unknown:"
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:799
|
||
msgid ""
|
||
"Starting with a 50% `prior probability <https://en.wikipedia.org/wiki/"
|
||
"Prior_probability>`_ of being male or female, we compute the posterior as "
|
||
"the prior times the product of likelihoods for the feature measurements "
|
||
"given the gender:"
|
||
msgstr ""
|
||
|
||
#: ../Doc/library/statistics.rst:814
|
||
msgid ""
|
||
"The final prediction goes to the largest posterior. This is known as the "
|
||
"`maximum a posteriori <https://en.wikipedia.org/wiki/"
|
||
"Maximum_a_posteriori_estimation>`_ or MAP:"
|
||
msgstr ""
|