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#: ../../source/reference/tensor/generated/mars.tensor.random.noncentral_f.rst:2
msgid "mars.tensor.random.noncentral\\_f"
msgstr ""

#: mars.tensor.random.noncentral_f:1 of
msgid "Draw samples from the noncentral F distribution."
msgstr ""

#: mars.tensor.random.noncentral_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 > 1. `nonc` is the non-centrality"
" parameter."
msgstr ""

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

#: mars.tensor.random.noncentral_f:8 of
msgid "Numerator degrees of freedom, should be > 0."
msgstr ""

#: mars.tensor.random.noncentral_f:10 of
msgid "Denominator degrees of freedom, should be > 0."
msgstr ""

#: mars.tensor.random.noncentral_f:12 of
msgid ""
"Non-centrality parameter, the sum of the squares of the numerator means, "
"should be >= 0."
msgstr ""

#: mars.tensor.random.noncentral_f:15 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``, ``dfden``, and ``nonc`` are all scalars.  "
"Otherwise, ``np.broadcast(dfnum, dfden, nonc).size`` samples are drawn."
msgstr ""

#: mars.tensor.random.noncentral_f:21 of
msgid "Desired chunk size on each dimension"
msgstr ""

#: mars.tensor.random.noncentral_f:23 of
msgid "Allocate the tensor on GPU if True, False as default"
msgstr ""

#: mars.tensor.random.noncentral_f:25 of
msgid "Data-type of the returned tensor."
msgstr ""

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

#: mars.tensor.random.noncentral_f:28 of
msgid ""
"**out** -- Drawn samples from the parameterized noncentral Fisher "
"distribution."
msgstr ""

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

#: mars.tensor.random.noncentral_f:32 of
msgid "Notes"
msgstr ""

#: mars.tensor.random.noncentral_f:33 of
msgid ""
"When calculating the power of an experiment (power = probability of "
"rejecting the null hypothesis when a specific alternative is true) the "
"non-central F statistic becomes important.  When the null hypothesis is "
"true, the F statistic follows a central F distribution. When the null "
"hypothesis is not true, then it follows a non-central F statistic."
msgstr ""

#: mars.tensor.random.noncentral_f:40 of
msgid "References"
msgstr ""

#: mars.tensor.random.noncentral_f:41 of
msgid ""
"Weisstein, Eric W. \"Noncentral F-Distribution.\" From MathWorld--A "
"Wolfram Web Resource. http://mathworld.wolfram.com/NoncentralF-"
"Distribution.html"
msgstr ""

#: mars.tensor.random.noncentral_f:44 of
msgid ""
"Wikipedia, \"Noncentral F-distribution\", http://en.wikipedia.org/wiki"
"/Noncentral_F-distribution"
msgstr ""

#: mars.tensor.random.noncentral_f:48 of
msgid "Examples"
msgstr ""

#: mars.tensor.random.noncentral_f:49 of
msgid ""
"In a study, testing for a specific alternative to the null hypothesis "
"requires use of the Noncentral F distribution. We need to calculate the "
"area in the tail of the distribution that exceeds the value of the F "
"distribution for the null hypothesis.  We'll plot the two probability "
"distributions for comparison."
msgstr ""

