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Pearson's correlation, denoted as ρ x, y), ρ ( x, y ) = K ∑ i = 1 K X i Y i - ( ∑ i = 1 K X i ) ( ∑ i = 1 K Y i ) [ K ∑ i = 1 K X i 2 - ( ∑ i = 1 K X i ) 2 ] [ K ∑ i = 1 K Y i 2 - ( ∑ i = 1 K Y i ) 2 ] (12). is employed to represent correlation between two images where ρ x, y) is a correlation coefficient (CC) between x and y, X is an image 1, Y is an image 2, and K is the number of image bits.
The zero-inflated parameter estimates, in contrast, represent correlation between the variables and a zero count.
However, these networks only represent correlation between genes but not necessarily physical interactions.
Edges represent correlation between miRNAs and mRNAs, the color of the edges designate the type of interaction.
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Data from double arrows represent correlations between the variables whereas data from single arrows represent standardized regression weights (parameter estimates).
Data from double arrows represent correlations between the variables, where data from single arrows represents the standardized regression weight (parameter estimates).
Off-diagonal values represent correlations between constructs.
Bolded values represent correlations between items and their hypothesized subscales.
Underlined values represent correlations between items and their hypothesized subscales.
Underlined values represent correlations between patient self-report and parent proxy-report.
Underlined values represent correlations between child self-report and parent proxy-report subscales.
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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