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# This file is distributed under the same license as the mars package.
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#: ../../source/reference/tensor/generated/mars.tensor.random.f.rst:2
msgid "mars.tensor.random.f"
msgstr ""

#: mars.tensor.random.f:1 of
msgid "Draw samples from an F distribution."
msgstr ""

#: mars.tensor.random.f:3 of
msgid ""
"Samples are drawn from an F distribution with specified parameters, "
"`dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom"
" in denominator), where both parameters should be greater than zero."
msgstr ""

#: mars.tensor.random.f:8 of
msgid ""
"The random variate of the F distribution (also known as the Fisher "
"distribution) is a continuous probability distribution that arises in "
"ANOVA tests, and is the ratio of two chi-square variates."
msgstr ""

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

#: mars.tensor.random.f:13 of
msgid "Degrees of freedom in numerator, should be > 0."
msgstr ""

#: mars.tensor.random.f:15 of
msgid "Degrees of freedom in denominator, should be > 0."
msgstr ""

#: mars.tensor.random.f:17 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 ``dfnum`` and ``dfden`` are both scalars. Otherwise, "
"``np.broadcast(dfnum, dfden).size`` samples are drawn."
msgstr ""

#: mars.tensor.random.f:22 of
msgid "Desired chunk size on each dimension"
msgstr ""

#: mars.tensor.random.f:24 of
msgid "Allocate the tensor on GPU if True, False as default"
msgstr ""

#: mars.tensor.random.f:26 of
msgid "Data-type of the returned tensor."
msgstr ""

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

#: mars.tensor.random.f:29 of
msgid "**out** -- Drawn samples from the parameterized Fisher distribution."
msgstr ""

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

#: mars.tensor.random.f:34 of
msgid ":func:`scipy.stats.f`"
msgstr ""

#: mars.tensor.random.f:35 of
msgid ""
"probability density function, distribution or cumulative density "
"function, etc."
msgstr ""

#: mars.tensor.random.f:38 of
msgid "Notes"
msgstr ""

#: mars.tensor.random.f:39 of
msgid ""
"The F statistic is used to compare in-group variances to between-group "
"variances. Calculating the distribution depends on the sampling, and so "
"it is a function of the respective degrees of freedom in the problem.  "
"The variable `dfnum` is the number of samples minus one, the between-"
"groups degrees of freedom, while `dfden` is the within-groups degrees of "
"freedom, the sum of the number of samples in each group minus the number "
"of groups."
msgstr ""

#: mars.tensor.random.f:48 of
msgid "References"
msgstr ""

#: mars.tensor.random.f:49 of
msgid ""
"Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill, Fifth "
"Edition, 2002."
msgstr ""

#: mars.tensor.random.f:51 of
msgid "Wikipedia, \"F-distribution\", http://en.wikipedia.org/wiki/F-distribution"
msgstr ""

#: mars.tensor.random.f:55 of
msgid "Examples"
msgstr ""

#: mars.tensor.random.f:56 of
msgid "An example from Glantz[1], pp 47-40:"
msgstr ""

#: mars.tensor.random.f:58 of
msgid ""
"Two groups, children of diabetics (25 people) and children from people "
"without diabetes (25 controls). Fasting blood glucose was measured, case "
"group had a mean value of 86.1, controls had a mean value of 82.2. "
"Standard deviations were 2.09 and 2.49 respectively. Are these data "
"consistent with the null hypothesis that the parents diabetic status does"
" not affect their children's blood glucose levels? Calculating the F "
"statistic from the data gives a value of 36.01."
msgstr ""

#: mars.tensor.random.f:66 of
msgid "Draw samples from the distribution:"
msgstr ""

#: mars.tensor.random.f:74 of
#, python-format
msgid "The lower bound for the top 1% of the samples is :"
msgstr ""

#: mars.tensor.random.f:79 of
#, python-format
msgid ""
"So there is about a 1% chance that the F statistic will exceed 7.62, the "
"measured value is 36, so the null hypothesis is rejected at the 1% level."
msgstr ""

