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Cumulative standard deviation: Difference between revisions
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Fixed lang tags.
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=={{header|Ada}}==
<lang ada>with Ada.Numerics.Elementary_Functions; use Ada.Numerics.Elementary_Functions;▼
▲with Ada.Numerics.Elementary_Functions; use Ada.Numerics.Elementary_Functions;
with Ada.Text_IO; use Ada.Text_IO;
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end loop;
Put_Line ("Deviation" & Float'Image (Deviation (Data)));
end Test_Deviation;</lang>
Sample output:
<pre>
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J is block-oriented; it expresses algorithms with the semantics of all the data being available at once. It does not have native lexical closure or coroutine semantics. It is possible to implement these semantics in J; see [[Moving Average]] for an example. We will not reprise that here.
<lang j> mean=: +/ % #
{{trans|R}}<BR>
Or we could take a cue from the [[#R|R implementation]] and reverse the Bessel correction to stddev:
<lang j> require'stats'
=={{header|Java}}==
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(7, 1.3997084244475311)
(9, 2.0)
>>>
===Using a class instance===
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=={{header|R}}==
===Built-in Std Dev fn===
<lang R>#The built-in standard deviation function applies the Bessel correction. To reverse this, we can apply an uncorrection.▼
▲#The built-in standard deviation function applies the Bessel correction. To reverse this, we can apply an uncorrection.
#If na.rm is true, missing data points (NA values) are removed.
reverseBesselCorrection <- function(x, na.rm=FALSE)
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}
testdata <- c(2,4,4,4,5,5,7,9)
reverseBesselCorrection(testdata)*sd(testdata) #2</lang>
===From scratch===
<lang R>#Again, if na.rm is true, missing data points (NA values) are removed.▼
▲#Again, if na.rm is true, missing data points (NA values) are removed.
uncorrectedsd <- function(x, na.rm=FALSE)
{
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usd
}
uncorrectedsd(testdata) #2</lang>
=={{header|REXX}}==
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This is indeed Object REXX.
<lang
x = .array~of(2,4,4,4,5,5,7,9)
sd = 0
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