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These variance parameters are then used to obtain a weighted least squares estimate of the regression parameters of a linear model.
These variance measurements have been made with samples of quite modest size, which should be a source of concern because higher-order statistics, such as variance, are poorly estimated from small sample sizes.
These variance stabilized data was used for clustering the transcripts.
These variance components are obtained from G*, P*, H* and R*.
These variance components must be specified in advance, for example, from twin and family studies.
These variance components are allowed to vary depending on the level of the moderator.
These variance components were used to calculate broad-sense heritability based on the population means [ 59].
These variance components are relatively easy to interpret for analysis of continuous outcomes using linear models.
These variance components were used in a second step to jointly estimate all the effects included in model (1).
These variance components increased non-linearly with age, with the male PE variance being higher than that of females.
These variance components may be estimated from the comparison of phenotypic similarity in MZ and DZ twins (Plomin, DeFries, Knopik, & Neiderhiser, 2013).
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