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The key parameters for this factor are the correlation between the two measures and the variance ratio of the dependent measure on the predictor.
Table 5 presents the construct reliability (CR) and average variance extracted (AVE) from latent variables, while the off-diagonal elements are the correlation between latent variables.
The square roots of average variance extracted (AVE) of latent variables are shown in Table 5, while the off-diagonal elements are the correlation between latent variables.
Major DFT contributions to the DLC research are the correlation between density and electronic structure as well as identification of reaction products formed during atmospheric exposure to be the main cause for low friction at elevated temperatures.
Loadings are the correlation between the phenotypic data and the component scores on the transformed space.
The diagonal value in bold is the square root of AVE while other values are the correlation between the constructs.
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Perhaps the most interesting results in Table 1 are the correlations between the dependent variables.
Factor loadings are the correlations between each variable and the factor.
The other new findings from this study are the correlations between the cytokine responses to various mycobacterial antigens and PHA.
ρ01, ρ02 and ρ12 are the correlations between these parameters.
Also given are the correlations between EDM and COR over these respective datasets with K = 0.1.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com