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The difference in principal component variances was used to represent the pattern of carbon utilization.
The percent contribution from the first and second principal component variances were 83.94 and 8.82 %, respectively.
The first and second principal component variances contained the information of carbon type and their contribution.
For bivariate independent component distributions, the asymptotic bias of the correlation coefficient estimators based on principal component variances is derived.
Having a qualification of any type and higher quality of life was associated with smaller random component variances.
The predictions of fluctuating velocity component variances, mean species concentrations, segregation coefficient and ionic species variance are in close agreement with existing experimental data.
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Especially in the transverse component, variance reduction was significantly smaller than other two components.
We find that MCP is an important factor for CH4 component variance, especially CH4 component variance is solely caused by MCP for reservoirs without adsorption gas, which shows that MCP can improve interpretation accuracy based on the measured composition.
The second principal component, representing the second largest component variance in the dataset (6.36%), revealed that the control samples grouped together separately from the post-surgery samples.
Heterog: unstructured component variance.
The principal component variance of z is 0.07, compared to ~10-5 for both x and y.
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