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The weighted mean in this case is: ¯ = ¯ (=), (where the order of the matrix–vector product is not commutative), in terms of the covariance of the weighted mean: ¯ = (=), For example, consider the weighted mean of the point [1 0] with high variance in the second component and [0 1] with high variance in the first component.
The genotype frequencies of the combined population are a weighted mean of the subpopulation frequencies, corresponding to a point somewhere on the solid line connecting 1 and 2. This point always has a lower heterozygosity (y value) than the corresponding (in allele frequency p ) Hardy-Weinberg equilibrium.
The result of this application of a weight function is a weighted sum or weighted average. Weight functions occur frequently in statistics and analysis, and are closely related to the concept of a measure. Weight functions can be employed in both discrete and continuous settings.
In survey research, the design effect is a number that shows how well a sample of people may represent a larger group of people for a specific measure of interest (such as the mean). This is important when the sample comes from a sampling method that is different than just picking people using a simple random sample .
Results from studies are combined using different approaches. One approach frequently used in meta-analysis in health care research is termed 'inverse variance method'. The average effect size across all studies is computed as a weighted mean, whereby the weights are equal to the inverse variance of each study's effect estimator. Larger studies ...
It can produce a weighted mean that has less variability than the arithmetic mean of a simple random sample of the population. In computational statistics , stratified sampling is a method of variance reduction when Monte Carlo methods are used to estimate population statistics from a known population.
Weighted networks and correlation networks can often be approximated by "factorizable" networks. [4] [7] Such approximations are often difficult to achieve for sparse, unweighted networks. Therefore, weighted (correlation) networks allow for a parsimonious parametrization (in terms of modules and module membership) (chapters 2, 6 in [1]) and. [8]
The first approach is to calculate a weighted generalized mean of the within-subunit species proportional abundances, and then take the inverse of this mean. The second approach is to calculate the species diversity for each subunit separately, and then take a weighted generalized mean of these. [4] [13] If the first approach is used, the ...