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The squared multiple correlations indicate the percentage of variation in a latent variable that is being explained by the model.
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Multivariate correlations indicate that multiple factors, including the ICMV of underlying layers and temperatures of HMA, affect the ICMV of the HMA layer.
Positive signs in these correlations indicate that identity disequilibria at multiple loci across the genome facilitated selection among genetically heterogeneous individuals (David, 1999; Szulkin et al., 2010).
The within-study correlations thus give an indication of the association between multiple endpoints within a study while the between-study correlations indicate how the underlying true study-specific effects on different outcomes vary jointly across studies.
These correlations indicate that the SHQ can evaluate the effects of multiple pathophysiological processes in sarcoidosis that involve several organ systems.
Negative genetic correlations indicate evolutionary trade-offs.
The observed correlations indicate good construct validity.
Significant correlations indicate co-evolution between organs.
Correlations indicated that years of experience were not correlated significantly with any of the five ratings provided after corrections for multiple tests.
a p < 0.001 for correlations indicated.
It is important to keep in mind that low squared multiple correlations do not necessarily indicate a poor model, rather, an incomplete one.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com