Mean and Standard Deviation

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The mean, &mu, of N numbers, A[1], ..., A[N], is their sum divided by N, i.e. (∑1..N A[i])/N. Their variance is (∑1..N (A[i]-μ)2)/N and their standard deviation, σ, is sqrt(variance); note that these quantities are always >=0. The mean gives the "centre of gravity" (CG) of the numbers, and the standard deviation indicates how far they stray from the CG, on average.

Both the mean and the standard deviation can be calculated on a single scan through A[ ] even though the mean is not known until the end of the scan:

= σ2 
= ( ∑i=1..N (A[i]-μ)2 ) / N
= ( (∑ A[i]2) - 2*μ*(∑ A[i]) + N*μ2 ) / N
= ( (∑ A[i]2) - 2*μ*sum ) / N + μ2
= ( ∑i=1..N A[i]2 ) / N - μ2
i.e. the mean squares minus the squared mean.
Hence σ = sqrt(sumSq / N - μ2)
where sumSq = ∑1..N A[i]2

This gives the following algorithm:

  sum := 0.0;
sumSq := 0.0;

for i in {1 .. N} do
     sum +:= A[i];
   sumSq +:= A[i]2
end for;

  mean := sum / N;
stdDev := sqrt(sumSq / N - mean2);

-- L.A., 1999


Coding Ockham's Razor, L. Allison, Springer

A Practical Introduction to Denotational Semantics, L. Allison, CUP

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© L. Allison   (or as otherwise indicated),
Faculty of Information Technology (Clayton), Monash University, Australia 3800 (6/'05 was School of Computer Science and Software Engineering, Fac. Info. Tech., Monash University,
was Department of Computer Science, Fac. Comp. & Info. Tech., '89 was Department of Computer Science, Fac. Sci., '68-'71 was Department of Information Science, Fac. Sci.)
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