# SOME DESCRIPTIVE TITLE.
# Copyright (C) 1999-2020, The Alibaba Group Holding Ltd.
# This file is distributed under the same license as the mars package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2020.
#
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msgid ""
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"Project-Id-Version: mars 0.5.0a2\n"
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"POT-Creation-Date: 2020-06-16 16:57+0800\n"
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#: ../../source/reference/tensor/generated/mars.tensor.random.standard_t.rst:2
msgid "mars.tensor.random.standard\\_t"
msgstr ""

#: mars.tensor.random.standard_t:1 of
msgid ""
"Draw samples from a standard Student's t distribution with `df` degrees "
"of freedom."
msgstr ""

#: mars.tensor.random.standard_t:4 of
msgid ""
"A special case of the hyperbolic distribution.  As `df` gets large, the "
"result resembles that of the standard normal distribution "
"(`standard_normal`)."
msgstr ""

#: mars.tensor.random.standard_t of
msgid "Parameters"
msgstr ""

#: mars.tensor.random.standard_t:8 of
msgid "Degrees of freedom, should be > 0."
msgstr ""

#: mars.tensor.random.standard_t:10 of
msgid ""
"Output shape.  If the given shape is, e.g., ``(m, n, k)``, then ``m * n *"
" k`` samples are drawn.  If size is ``None`` (default), a single value is"
" returned if ``df`` is a scalar.  Otherwise, ``mt.array(df).size`` "
"samples are drawn."
msgstr ""

#: mars.tensor.random.standard_t:15 of
msgid "Desired chunk size on each dimension"
msgstr ""

#: mars.tensor.random.standard_t:17 of
msgid "Allocate the tensor on GPU if True, False as default"
msgstr ""

#: mars.tensor.random.standard_t:19 of
msgid "Data-type of the returned tensor."
msgstr ""

#: mars.tensor.random.standard_t of
msgid "Returns"
msgstr ""

#: mars.tensor.random.standard_t:22 of
msgid ""
"**out** -- Drawn samples from the parameterized standard Student's t "
"distribution."
msgstr ""

#: mars.tensor.random.standard_t of
msgid "Return type"
msgstr ""

#: mars.tensor.random.standard_t:26 of
msgid "Notes"
msgstr ""

#: mars.tensor.random.standard_t:27 of
msgid "The probability density function for the t distribution is"
msgstr ""

#: mars.tensor.random.standard_t:29 of
msgid ""
"P(x, df) = \\frac{\\Gamma(\\frac{df+1}{2})}{\\sqrt{\\pi df} "
"\\Gamma(\\frac{df}{2})}\\Bigl( 1+\\frac{x^2}{df} \\Bigr)^{-(df+1)/2}"
msgstr ""

#: mars.tensor.random.standard_t:32 of
msgid ""
"The t test is based on an assumption that the data come from a Normal "
"distribution. The t test provides a way to test whether the sample mean "
"(that is the mean calculated from the data) is a good estimate of the "
"true mean."
msgstr ""

#: mars.tensor.random.standard_t:37 of
msgid ""
"The derivation of the t-distribution was first published in 1908 by "
"William Gosset while working for the Guinness Brewery in Dublin. Due to "
"proprietary issues, he had to publish under a pseudonym, and so he used "
"the name Student."
msgstr ""

#: mars.tensor.random.standard_t:43 of
msgid "References"
msgstr ""

#: mars.tensor.random.standard_t:44 of
msgid "Dalgaard, Peter, \"Introductory Statistics With R\", Springer, 2002."
msgstr ""

#: mars.tensor.random.standard_t:46 of
msgid ""
"Wikipedia, \"Student's t-distribution\" "
"http://en.wikipedia.org/wiki/Student's_t-distribution"
msgstr ""

#: mars.tensor.random.standard_t:50 of
msgid "Examples"
msgstr ""

#: mars.tensor.random.standard_t:51 of
msgid ""
"From Dalgaard page 83 [1]_, suppose the daily energy intake for 11 women "
"in Kj is:"
msgstr ""

#: mars.tensor.random.standard_t:59 of
msgid ""
"Does their energy intake deviate systematically from the recommended "
"value of 7725 kJ?"
msgstr ""

#: mars.tensor.random.standard_t:62 of
#, python-format
msgid ""
"We have 10 degrees of freedom, so is the sample mean within 95% of the "
"recommended value?"
msgstr ""

#: mars.tensor.random.standard_t:71 of
msgid ""
"Calculate the t statistic, setting the ddof parameter to the unbiased "
"value so the divisor in the standard deviation will be degrees of "
"freedom, N-1."
msgstr ""

#: mars.tensor.random.standard_t:79 of
msgid ""
"For a one-sided t-test, how far out in the distribution does the t "
"statistic appear?"
msgstr ""

#: mars.tensor.random.standard_t:85 of
#, python-format
msgid ""
"So the p-value is about 0.009, which says the null hypothesis has a "
"probability of about 99% of being true."
msgstr ""

