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Failure to control for this within-individual correlation in analyses could result in biased estimates of variability and incorrect inferences about the estimated effects in regression models.
This leads to biased estimates of variability in some infrapopulations.
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However, these approaches yield biased estimates of the parameters of interest and they underestimate the variability in the data set because the same value is imputed repeatedly [ 1- 3].
Approaches based on 16S rRNA sequencing, however, provide a biased estimate of microbial diversity due to the wide variability in copy number of the 16S gene even within closely related organisms, and due to amplification biases inherent in PCR.
As expected, is a biased estimate of.
Each would yield a particular type of biased estimate.
These findings suggest that insufficient information on the spatial variability of soil respiration and imbalanced sampling could bias estimates of current and future carbon budgets.
Large measurement errors bias estimates of theta towards zero.
This has the net effect of biasing estimates of λ.
Bollen, K. A. & Stine, R. Direct and Indirect Effects: Classical and Bootstrap Estimates of Variability.
The linear mixed model will provide two estimates of variability: cluster-level variability and residual variability.
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