Difference between revisions of "Variance"
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This can be estimated from a set of measurements as the average squared deviation by summing the individuals components and dividing by the total number of observations, ''n'', as ''σ''<sup>2</sup> = (1/''n'')∑(''x''<sub>''i''</sub>-''μ'')<sup>2</sup>, where ''x''<sub>''i''</sub> is each individual data-point as ''i'' goes from 1 to ''n''. | This can be estimated from a set of measurements as the average squared deviation by summing the individuals components and dividing by the total number of observations, ''n'', as ''σ''<sup>2</sup> = (1/''n'')∑(''x''<sub>''i''</sub>-''μ'')<sup>2</sup>, where ''x''<sub>''i''</sub> is each individual data-point as ''i'' goes from 1 to ''n''. | ||
| − | + | If the underlying factors that contribute to a traits variance are independent, then the total trait variance is a sum of the individual variance components. Example, ... | |
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Latest revision as of 21:37, 12 July 2014
The variance of a trait measured in a population is the squared deviation from the mean.
If the mean is μ.
The expected variance is σ2 = (x-μ)2 where x is an individual data-point.
This can be estimated from a set of measurements as the average squared deviation by summing the individuals components and dividing by the total number of observations, n, as σ2 = (1/n)∑(xi-μ)2, where xi is each individual data-point as i goes from 1 to n.
If the underlying factors that contribute to a traits variance are independent, then the total trait variance is a sum of the individual variance components. Example, ...
