Algorithms for calculating variance

Algorithms for calculating variance play a major role in computational statistics. A key difficulty in the design of good algorithms for this problem is that formulas for the variance may involve sums of squares, which can lead to numerical instability as well as to arithmetic overflow when dealing with large values. Naïve algorithm A formula for calculating the variance of an entire population of size N is: :\sigma^2 = \overline{(x^2)} - \bar x^2 = \frac {\sum_{i=1}^N x_i^2 - (\sum_{i=1}^N x_i)^2/N}{N}. Using Bessel's correction to calculate an unbiased estimate of the population variance from a finite sample of n observations, the formula is: :s^2 = \left(\frac {\sum_{i=1}^n x_i^2} n - \left( \frac {\sum_{i=1}^n x_i} n \right)^2\right) \cdot \frac {n}{n-1}. Therefore, a naïve algorithm to calculate the estimated variance is given by the following:

Let n ← 0, Sum ← 0, SumSq ← 0
For each datum x: ** n ← n + 1 ** Sum ← Sum + x

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Algorithms for calculating variance

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