The mean, μ, of N≥1 real 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 √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:

variance

= 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 square minus the squared mean.

 

Hence σ = √{ sumSq/N - μ² }

where sumSq = ∑_1..N A[i]²

 

To remember this: "The variance equals the mean square minus the squared mean."

 

It 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);

Notes

• See [segmentation].