Statistics/Basic: Difference between revisions

Line 4,132:

As can be seen, increasing the sample size reduces the variation between the buckets, showing that the <code>rand()</code> function at least approximates a uniform distribution. (Because Tcl 8.5 supports arbitrary precision integer arithmetic there is no reason in principle why the details for a trillion numbers couldn't be calculated, but it would take quite a while.)

=={{header|VBA}}==

<lang vb>Option Base 1

Private Function mean(s() As Variant) As Double

mean = WorksheetFunction.Average(s)

End Function

Private Function standard_deviation(s() As Variant) As Double

standard_deviation = WorksheetFunction.StDev(s)

End Function

Public Sub basic_statistics()

Dim s() As Variant

For e = 2 To 4

ReDim s(10 ^ e)

For i = 1 To 10 ^ e

s(i) = Rnd()

Next i

Debug.Print "sample size"; UBound(s), "mean"; mean(s), "standard deviation"; standard_deviation(s)

t = WorksheetFunction.Frequency(s, [{0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0}])

For i = 1 To 10

Debug.Print Format((i - 1) / 10, "0.00");

Debug.Print "-"; Format(i / 10, "0.00"),

Debug.Print String$(t(i, 1) / (10 ^ (e - 2)), "X");

Debug.Print

Next i

Debug.Print

Next e

End Sub</lang>{{out}}

<pre>sample size 100 mean 0,472405961751938 standard deviation 0,260463885857138

0,00-0,10 XXXXXX

0,10-0,20 XXXXXXXXX

0,20-0,30 XXXXXXXXXXXXXXX

0,30-0,40 XXXXXXXXXXXXXXX

0,40-0,50 XXXXXXXXXXXXXX

0,50-0,60 XXXXXXX

0,60-0,70 XXXXXXXXXXX

0,70-0,80 XXXXXXXX

0,80-0,90 XXXXXXXXXX

0,90-1,00 XXXXX

sample size 1000 mean 0,500459910154343 standard deviation 0,278991757028358

0,00-0,10 XXXXXXXX

0,10-0,20 XXXXXXXXXX

0,20-0,30 XXXXXXXXXX

0,30-0,40 XXXXXXXXXX

0,40-0,50 XXXXXXXXXX

0,50-0,60 XXXXXXXXXXXX

0,60-0,70 XXXXXXXXXXX

0,70-0,80 XXXXXXXXX

0,80-0,90 XXXXXXXXX

0,90-1,00 XXXXXXXXXX

sample size 10000 mean 0,496753623914719 standard deviation 0,28740805585887

0,00-0,10 XXXXXXXXXX

0,10-0,20 XXXXXXXXXX

0,20-0,30 XXXXXXXXXX

0,30-0,40 XXXXXXXXXX

0,40-0,50 XXXXXXXXXX

0,50-0,60 XXXXXXXXXX

0,60-0,70 XXXXXXXXXX

0,70-0,80 XXXXXXXXXX

0,80-0,90 XXXXXXXXXX

0,90-1,00 XXXXXXXXXX</pre>

=={{header|zkl}}==

<lang zkl>fcn mean(ns) { ns.sum(0.0)/ns.len() }