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The Allan variance is a common and standard method to analyze FOGs, but it cannot be used to analyze the dynamic characteristics.
It would be interesting to see whether increased phenotypic variance is a common pattern in other species' range expansions, and whether there are underlying correlated genetic effects similar to those seen in wing patterning in the pale grass blue butterfly [ 1].
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The health-services researcher is therefore likely to conclude that if we measured more variables, we could explain more of this variance, while improvement scientists are more likely to conclude that this unexplained variance is a reflection of common-cause variation in a good process that is under control.
Variance structure is a common term in data analysis.
Analysis of variance (ANOVA) is a common statistical technique in physiological research, and often one or more of the independent/predictor variables such as dose, time, or age, can be treated as a continuous, rather than a categorical variable during analysis – even if subjects were randomly assigned to treatment groups.
This indicates that increased methylation variance in cases is a common feature for obesity and cancer, but that the increased methylation levels seem to be platform dependent.
This bias-variance trade-off is a common problem in choosing an estimator, but we think that unbiased ness is an important goal in genomewide association studies for two main reasons.
PCA helps to explain the variance in data and is a common technique for dimensionality reduction in high dimensional data.
(ii) There is a common variance structure (eq 28) across different data sources.
Over-dispersion, i.e., variance larger than the mean, is a common phenomenon in observed count data [ 7].
Over-dispersion relative to the Poisson distribution (i.e. where the variance is greater than the mean) is a common feature among real data.
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