# Copyright (C) 2001-2018, Python Software Foundation # For licence information, see README file. # msgid "" msgstr "" "Project-Id-Version: Python 3\n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2020-02-04 10:00+0100\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language-Team: FRENCH \n" "Language: fr\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=UTF-8\n" "Content-Transfer-Encoding: 8bit\n" #: ../Doc/library/statistics.rst:2 msgid ":mod:`statistics` --- Mathematical statistics functions" msgstr "" #: ../Doc/library/statistics.rst:12 msgid "**Source code:** :source:`Lib/statistics.py`" msgstr "**Code source :** :source:`Lib/statistics.py`" #: ../Doc/library/statistics.rst:21 msgid "" "This module provides functions for calculating mathematical statistics of " "numeric (:class:`~numbers.Real`-valued) data." msgstr "" #: ../Doc/library/statistics.rst:24 msgid "" "The module is not intended to be a competitor to third-party libraries such " "as `NumPy `_, `SciPy `_, or " "proprietary full-featured statistics packages aimed at professional " "statisticians such as Minitab, SAS and Matlab. It is aimed at the level of " "graphing and scientific calculators." msgstr "" #: ../Doc/library/statistics.rst:30 msgid "" "Unless explicitly noted, these functions support :class:`int`, :class:" "`float`, :class:`~decimal.Decimal` and :class:`~fractions.Fraction`. " "Behaviour with other types (whether in the numeric tower or not) is " "currently unsupported. Collections with a mix of types are also undefined " "and implementation-dependent. If your input data consists of mixed types, " "you may be able to use :func:`map` to ensure a consistent result, for " "example: ``map(float, input_data)``." msgstr "" #: ../Doc/library/statistics.rst:39 msgid "Averages and measures of central location" msgstr "" #: ../Doc/library/statistics.rst:41 msgid "" "These functions calculate an average or typical value from a population or " "sample." msgstr "" #: ../Doc/library/statistics.rst:45 msgid ":func:`mean`" msgstr "" #: ../Doc/library/statistics.rst:45 msgid "Arithmetic mean (\"average\") of data." msgstr "" #: ../Doc/library/statistics.rst:46 msgid ":func:`fmean`" msgstr "" #: ../Doc/library/statistics.rst:46 msgid "Fast, floating point arithmetic mean." msgstr "" #: ../Doc/library/statistics.rst:47 msgid ":func:`geometric_mean`" msgstr "" #: ../Doc/library/statistics.rst:47 msgid "Geometric mean of data." msgstr "" #: ../Doc/library/statistics.rst:48 msgid ":func:`harmonic_mean`" msgstr "" #: ../Doc/library/statistics.rst:48 msgid "Harmonic mean of data." msgstr "" #: ../Doc/library/statistics.rst:49 msgid ":func:`median`" msgstr "" #: ../Doc/library/statistics.rst:49 msgid "Median (middle value) of data." msgstr "" #: ../Doc/library/statistics.rst:50 msgid ":func:`median_low`" msgstr "" #: ../Doc/library/statistics.rst:50 msgid "Low median of data." msgstr "" #: ../Doc/library/statistics.rst:51 msgid ":func:`median_high`" msgstr "" #: ../Doc/library/statistics.rst:51 msgid "High median of data." msgstr "" #: ../Doc/library/statistics.rst:52 msgid ":func:`median_grouped`" msgstr "" #: ../Doc/library/statistics.rst:52 msgid "Median, or 50th percentile, of grouped data." msgstr "" #: ../Doc/library/statistics.rst:53 msgid ":func:`mode`" msgstr "" #: ../Doc/library/statistics.rst:53 msgid "Single mode (most common value) of discrete or nominal data." msgstr "" #: ../Doc/library/statistics.rst:54 msgid ":func:`multimode`" msgstr "" #: ../Doc/library/statistics.rst:54 msgid "List of modes (most common values) of discrete or nomimal data." msgstr "" #: ../Doc/library/statistics.rst:55 msgid ":func:`quantiles`" msgstr "" #: ../Doc/library/statistics.rst:55 msgid "Divide data into intervals with equal probability." msgstr "" #: ../Doc/library/statistics.rst:59 msgid "Measures of spread" msgstr "" #: ../Doc/library/statistics.rst:61 msgid "" "These functions calculate a measure of how much the population or sample " "tends to deviate from the typical or average values." msgstr "" #: ../Doc/library/statistics.rst:65 msgid ":func:`pstdev`" msgstr "" #: ../Doc/library/statistics.rst:65 msgid "Population standard deviation of data." msgstr "" #: ../Doc/library/statistics.rst:66 msgid ":func:`pvariance`" msgstr "" #: ../Doc/library/statistics.rst:66 msgid "Population variance of data." msgstr "" #: ../Doc/library/statistics.rst:67 msgid ":func:`stdev`" msgstr "" #: ../Doc/library/statistics.rst:67 msgid "Sample standard deviation of data." msgstr "" #: ../Doc/library/statistics.rst:68 msgid ":func:`variance`" msgstr "" #: ../Doc/library/statistics.rst:68 msgid "Sample variance of data." msgstr "" #: ../Doc/library/statistics.rst:73 msgid "Function details" msgstr "" #: ../Doc/library/statistics.rst:75 msgid "" "Note: The functions do not require the data given to them to be sorted. " "However, for reading convenience, most of the examples show sorted sequences." msgstr "" #: ../Doc/library/statistics.rst:80 msgid "" "Return the sample arithmetic mean of *data* which can be a sequence or " "iterable." msgstr "" #: ../Doc/library/statistics.rst:82 msgid "" "The arithmetic mean is the sum of the data divided by the number of data " "points. It is commonly called \"the average\", although it is only one of " "many different mathematical averages. It is a measure of the central " "location of the data." msgstr "" #: ../Doc/library/statistics.rst:87 msgid "If *data* is empty, :exc:`StatisticsError` will be raised." msgstr "" #: ../Doc/library/statistics.rst:89 msgid "Some examples of use:" msgstr "" #: ../Doc/library/statistics.rst:108 msgid "" "The mean is strongly affected by outliers and is not a robust estimator for " "central location: the mean is not necessarily a typical example of the data " "points. For more robust measures of central location, see :func:`median` " "and :func:`mode`." msgstr "" #: ../Doc/library/statistics.rst:113 msgid "" "The sample mean gives an unbiased estimate of the true population mean, so " "that when taken on average over all the possible samples, ``mean(sample)`` " "converges on the true mean of the entire population. If *data* represents " "the entire population rather than a sample, then ``mean(data)`` is " "equivalent to calculating the true population mean μ." msgstr "" #: ../Doc/library/statistics.rst:122 msgid "Convert *data* to floats and compute the arithmetic mean." msgstr "" #: ../Doc/library/statistics.rst:124 msgid "" "This runs faster than the :func:`mean` function and it always returns a :" "class:`float`. The *data* may be a sequence or iterable. If the input " "dataset is empty, raises a :exc:`StatisticsError`." msgstr "" #: ../Doc/library/statistics.rst:138 msgid "Convert *data* to floats and compute the geometric mean." msgstr "" #: ../Doc/library/statistics.rst:140 msgid "" "The geometric mean indicates the central tendency or typical value of the " "*data* using the product of the values (as opposed to the arithmetic mean " "which uses their sum)." msgstr "" #: ../Doc/library/statistics.rst:144 msgid "" "Raises a :exc:`StatisticsError` if the input dataset is empty, if it " "contains a zero, or if it contains a negative value. The *data* may be a " "sequence or iterable." msgstr "" #: ../Doc/library/statistics.rst:148 msgid "" "No special efforts are made to achieve exact results. (However, this may " "change in the future.)" msgstr "" #: ../Doc/library/statistics.rst:161 msgid "" "Return the harmonic mean of *data*, a sequence or iterable of real-valued " "numbers." msgstr "" #: ../Doc/library/statistics.rst:164 msgid "" "The harmonic mean, sometimes called the subcontrary mean, is the reciprocal " "of the arithmetic :func:`mean` of the reciprocals of the data. For example, " "the harmonic mean of three values *a*, *b* and *c* will be equivalent to ``3/" "(1/a + 1/b + 1/c)``. If one of the values is zero, the result will be zero." msgstr "" #: ../Doc/library/statistics.rst:170 msgid "" "The harmonic mean is a type of average, a measure of the central location of " "the data. It is often appropriate when averaging rates or ratios, for " "example speeds." msgstr "" #: ../Doc/library/statistics.rst:174 msgid "" "Suppose a car travels 10 km at 40 km/hr, then another 10 km at 60 km/hr. " "What is the average speed?" msgstr "" #: ../Doc/library/statistics.rst:182 msgid "" "Suppose an investor purchases an equal value of shares in each of three " "companies, with P/E (price/earning) ratios of 2.5, 3 and 10. What is the " "average P/E ratio for the investor's portfolio?" msgstr "" #: ../Doc/library/statistics.rst:191 msgid "" ":exc:`StatisticsError` is raised if *data* is empty, or any element is less " "than zero." msgstr "" #: ../Doc/library/statistics.rst:194 msgid "" "The current algorithm has an early-out when it encounters a zero in the " "input. This means that the subsequent inputs are not tested for validity. " "(This behavior may change in the future.)" msgstr "" #: ../Doc/library/statistics.rst:203 msgid "" "Return the median (middle value) of numeric data, using the common \"mean of " "middle two\" method. If *data* is empty, :exc:`StatisticsError` is raised. " "*data* can be a sequence or iterable." msgstr "" #: ../Doc/library/statistics.rst:207 msgid "" "The median is a robust measure of central location and is less affected by " "the presence of outliers. When the number of data points is odd, the middle " "data point is returned:" msgstr "" #: ../Doc/library/statistics.rst:216 msgid "" "When the number of data points is even, the median is interpolated by taking " "the average of the two middle values:" msgstr "" #: ../Doc/library/statistics.rst:224 msgid "" "This is suited for when your data is discrete, and you don't mind that the " "median may not be an actual data point." msgstr "" #: ../Doc/library/statistics.rst:227 msgid "" "If the data is ordinal (supports order operations) but not numeric (doesn't " "support addition), consider using :func:`median_low` or :func:`median_high` " "instead." msgstr "" #: ../Doc/library/statistics.rst:233 msgid "" "Return the low median of numeric data. If *data* is empty, :exc:" "`StatisticsError` is raised. *data* can be a sequence or iterable." msgstr "" #: ../Doc/library/statistics.rst:236 msgid "" "The low median is always a member of the data set. When the number of data " "points is odd, the middle value is returned. When it is even, the smaller " "of the two middle values is returned." msgstr "" #: ../Doc/library/statistics.rst:247 msgid "" "Use the low median when your data are discrete and you prefer the median to " "be an actual data point rather than interpolated." msgstr "" #: ../Doc/library/statistics.rst:253 msgid "" "Return the high median of data. If *data* is empty, :exc:`StatisticsError` " "is raised. *data* can be a sequence or iterable." msgstr "" #: ../Doc/library/statistics.rst:256 msgid "" "The high median is always a member of the data set. When the number of data " "points is odd, the middle value is returned. When it is even, the larger of " "the two middle values is returned." msgstr "" #: ../Doc/library/statistics.rst:267 msgid "" "Use the high median when your data are discrete and you prefer the median to " "be an actual data point rather than interpolated." msgstr "" #: ../Doc/library/statistics.rst:273 msgid "" "Return the median of grouped continuous data, calculated as the 50th " "percentile, using interpolation. If *data* is empty, :exc:`StatisticsError` " "is raised. *data* can be a sequence or iterable." msgstr "" #: ../Doc/library/statistics.rst:282 msgid "" "In the following example, the data are rounded, so that each value " "represents the midpoint of data classes, e.g. 1 is the midpoint of the class " "0.5--1.5, 2 is the midpoint of 1.5--2.5, 3 is the midpoint of 2.5--3.5, " "etc. With the data given, the middle value falls somewhere in the class " "3.5--4.5, and interpolation is used to estimate it:" msgstr "" #: ../Doc/library/statistics.rst:293 msgid "" "Optional argument *interval* represents the class interval, and defaults to " "1. Changing the class interval naturally will change the interpolation:" msgstr "" #: ../Doc/library/statistics.rst:303 msgid "" "This function does not check whether the data points are at least *interval* " "apart." msgstr "" #: ../Doc/library/statistics.rst:308 msgid "" "Under some circumstances, :func:`median_grouped` may coerce data points to " "floats. This behaviour is likely to change in the future." msgstr "" #: ../Doc/library/statistics.rst:313 msgid "" "\"Statistics for the Behavioral Sciences\", Frederick J Gravetter and Larry " "B Wallnau (8th Edition)." msgstr "" #: ../Doc/library/statistics.rst:316 msgid "" "The `SSMEDIAN `_ function in the Gnome Gnumeric " "spreadsheet, including `this discussion `_." msgstr "" #: ../Doc/library/statistics.rst:324 msgid "" "Return the single most common data point from discrete or nominal *data*. " "The mode (when it exists) is the most typical value and serves as a measure " "of central location." msgstr "" #: ../Doc/library/statistics.rst:328 msgid "" "If there are multiple modes with the same frequency, returns the first one " "encountered in the *data*. If the smallest or largest of those is desired " "instead, use ``min(multimode(data))`` or ``max(multimode(data))``. If the " "input *data* is empty, :exc:`StatisticsError` is raised." msgstr "" #: ../Doc/library/statistics.rst:333 msgid "" "``mode`` assumes discrete data and returns a single value. This is the " "standard treatment of the mode as commonly taught in schools:" msgstr "" #: ../Doc/library/statistics.rst:341 msgid "" "The mode is unique in that it is the only statistic in this package that " "also applies to nominal (non-numeric) data:" msgstr "" #: ../Doc/library/statistics.rst:349 msgid "" "Now handles multimodal datasets by returning the first mode encountered. " "Formerly, it raised :exc:`StatisticsError` when more than one mode was found." msgstr "" #: ../Doc/library/statistics.rst:357 msgid "" "Return a list of the most frequently occurring values in the order they were " "first encountered in the *data*. Will return more than one result if there " "are multiple modes or an empty list if the *data* is empty:" msgstr "" #: ../Doc/library/statistics.rst:373 msgid "" "Return the population standard deviation (the square root of the population " "variance). See :func:`pvariance` for arguments and other details." msgstr "" #: ../Doc/library/statistics.rst:384 msgid "" "Return the population variance of *data*, a non-empty sequence or iterable " "of 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:390 msgid "" "If the optional second argument *mu* is given, it is typically the mean of " "the *data*. It can also be used to compute the second moment around a point " "that is not the mean. If it is missing or ``None`` (the default), the " "arithmetic mean is automatically calculated." msgstr "" #: ../Doc/library/statistics.rst:395 msgid "" "Use this function to calculate the variance from the entire population. To " "estimate the variance from a sample, the :func:`variance` function is " "usually a better choice." msgstr "" #: ../Doc/library/statistics.rst:399 msgid "Raises :exc:`StatisticsError` if *data* is empty." msgstr "" #: ../Doc/library/statistics.rst:401 ../Doc/library/statistics.rst:471 msgid "Examples:" msgstr "Exemples :" #: ../Doc/library/statistics.rst:409 msgid "" "If you have already calculated the mean of your data, you can pass it as the " "optional second argument *mu* to avoid recalculation:" msgstr "" #: ../Doc/library/statistics.rst:418 msgid "Decimals and Fractions are supported:" msgstr "" #: ../Doc/library/statistics.rst:432 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:436 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:445 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:456 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:462 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:466 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:469 msgid "Raises :exc:`StatisticsError` if *data* has fewer than two values." msgstr "" #: ../Doc/library/statistics.rst:479 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:488 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:492 msgid "Decimal and Fraction values are supported:" msgstr "" #: ../Doc/library/statistics.rst:506 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:511 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:517 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:520 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:525 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:529 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:534 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:538 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:545 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:569 msgid "Exceptions" msgstr "Exceptions" #: ../Doc/library/statistics.rst:571 msgid "A single exception is defined:" msgstr "" #: ../Doc/library/statistics.rst:575 msgid "Subclass of :exc:`ValueError` for statistics-related exceptions." msgstr "" #: ../Doc/library/statistics.rst:579 msgid ":class:`NormalDist` objects" msgstr "" #: ../Doc/library/statistics.rst:581 msgid "" ":class:`NormalDist` is a tool for creating and manipulating normal " "distributions of a `random variable `_. It is a class that treats the mean and " "standard deviation of data measurements as a single entity." msgstr "" #: ../Doc/library/statistics.rst:587 msgid "" "Normal distributions arise from the `Central Limit Theorem `_ and have a wide range of " "applications in statistics." msgstr "" #: ../Doc/library/statistics.rst:593 msgid "" "Returns a new *NormalDist* object where *mu* represents the `arithmetic mean " "`_ and *sigma* represents the " "`standard deviation `_." msgstr "" #: ../Doc/library/statistics.rst:598 msgid "If *sigma* is negative, raises :exc:`StatisticsError`." msgstr "" #: ../Doc/library/statistics.rst:602 msgid "" "A read-only property for the `arithmetic mean `_ of a normal distribution." msgstr "" #: ../Doc/library/statistics.rst:608 msgid "" "A read-only property for the `median `_ of a normal distribution." msgstr "" #: ../Doc/library/statistics.rst:614 msgid "" "A read-only property for the `mode `_ of a normal distribution." msgstr "" #: ../Doc/library/statistics.rst:620 msgid "" "A read-only property for the `standard deviation `_ of a normal distribution." msgstr "" #: ../Doc/library/statistics.rst:626 msgid "" "A read-only property for the `variance `_ of a normal distribution. Equal to the square of the standard " "deviation." msgstr "" #: ../Doc/library/statistics.rst:632 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:635 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:643 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:646 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:652 msgid "" "Using a `probability density function (pdf) `_, 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:658 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:665 msgid "" "Using a `cumulative distribution function (cdf) `_, 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:672 msgid "" "Compute the inverse cumulative distribution function, also known as the " "`quantile function `_ or " "the `percent-point `_ function. Mathematically, it is written " "``x : P(X <= x) = p``." msgstr "" #: ../Doc/library/statistics.rst:678 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:684 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 `_." msgstr "" #: ../Doc/library/statistics.rst:691 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:695 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:699 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:709 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:712 msgid "" "Since normal distributions arise from additive effects of independent " "variables, it is possible to `add and subtract two independent normally " "distributed random variables `_ represented as instances of :" "class:`NormalDist`. For example:" msgstr "" #: ../Doc/library/statistics.rst:732 msgid ":class:`NormalDist` Examples and Recipes" msgstr "" #: ../Doc/library/statistics.rst:734 msgid ":class:`NormalDist` readily solves classic probability problems." msgstr "" #: ../Doc/library/statistics.rst:736 msgid "" "For example, given `historical data for SAT exams `_ showing that scores are " "normally distributed with a mean of 1060 and a standard deviation of 195, " "determine the percentage of students with test scores between 1100 and 1200, " "after rounding to the nearest whole number:" msgstr "" #: ../Doc/library/statistics.rst:749 msgid "" "Find the `quartiles `_ and `deciles " "`_ for the SAT scores:" msgstr "" #: ../Doc/library/statistics.rst:759 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 `_:" msgstr "" #: ../Doc/library/statistics.rst:775 msgid "" "Normal distributions can be used to approximate `Binomial distributions " "`_ when the sample " "size is large and when the probability of a successful trial is near 50%." msgstr "" #: ../Doc/library/statistics.rst:780 msgid "" "For example, an open source conference has 750 attendees and two rooms with " "a 500 person capacity. There is a talk about Python and another about Ruby. " "In previous conferences, 65% of the attendees preferred to listen to Python " "talks. Assuming the population preferences haven't changed, what is the " "probability that the Python room will stay within its capacity limits?" msgstr "" #: ../Doc/library/statistics.rst:811 msgid "Normal distributions commonly arise in machine learning problems." msgstr "" #: ../Doc/library/statistics.rst:813 msgid "" "Wikipedia has a `nice example of a Naive Bayesian Classifier `_. 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:818 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:831 msgid "" "Next, we encounter a new person whose feature measurements are known but " "whose gender is unknown:" msgstr "" #: ../Doc/library/statistics.rst:840 msgid "" "Starting with a 50% `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:855 msgid "" "The final prediction goes to the largest posterior. This is known as the " "`maximum a posteriori `_ or MAP:" msgstr ""