For example, the mean and variance (as well as other statistical estimators) can be immediately computed and streamed out to the display stage.
As an example, variance components pertaining to, e.g. companies, occupations, subjects, days within subjects, and exposure samples within days have been determined for a large number of airborne, dermal, and biomechanical exposures in working life (e.g. [ 2, 3, 9- 15]).
However, the latter conjecture is not trivial, particularly for variables that are not necessarily well estimated over an extended period of time by averaging across embedded sub-periods, for example, variances [ 55] and, as we propose, percentiles.
However, a clear advantage of using microcosms to study Taylor's law is that sampling error can be rejected empirically in future experiments, for example, by plotting variance as a function of sampling effort for different values of mean abundance.
In the Methods section we show that the ssLNA applied to their example leads to the same variance as obtained from their reduced probability distribution.
If the null hypothesis in (1) is not rejected, the differential expression of the pathway is tested by the hypothesis H 0 : μ 1 = μ 2 subject to Σ 1 = Σ 2. This test can be performed by using exact procedures such as a multivariate analysis of variance (MANOVA), as, for example, in [ 8].
New high-throughput screening methods provide a similar advantage for screening within-individual components of variance as does traditional cloning see for example [ 45].
For example the variance of macroscopic circular and spherical samples may be deduced as may the correlation coefficient for linear macroscopic samples.
We need to learn to recognize variance as an opportunity.
So for example, the variance does not have to exist.
