20 Sep
2018
20 Sep
'18
3:32 p.m.
On 09/17/18 22:33, Steven Watanabe via Boost wrote:
You're thinking of the formula variance = \sum (x_i - mean)^2 / count = \sum x_i^2/count - mean^2
Indeed.
That formula doesn't apply in this case, since the variance is the variance of the bin count, not the variance of the weights. The estimate for the variance is described here: http://hdembinski.github.io/histogram/doc/html/histogram/rationale.html#hist...
Ok, so weights are used to increase the bin count by a certain amount, and the variance is an estimate of the spread of these weighted counts. I had initially assumed that the per-bin variance measured how much values that are put into a bin deviates from its center; e.g. the midpoint of the bin, or the bin average.