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These two are correlated, with a correlation value of r = −0.53 Fig. 14 Harmonic richness factor (HRF) versus fundamental Frequency (F0) for each speaker across the entire corpus.
Next, a direct comparison of the effects of physical task stress on the HRF and NAQ reveal they are strongly correlated, with a correlation value of r = −0.89, as is demonstrated by the scatter plot in Fig. 15.
Fig. 11 The normalized amplitude quotient (NAQ) shift versus exertion level: each point represents one speaker; there is significant variability in mean NAQ shift across speakers; however, this is not correlated with exertion level, with a correlation value of r = 0.108.
Fig. 12 The harmonic richness factor (HRF) shift versus exertion level: each point represents one speaker; there is significant variability in mean HRF shift across speakers; however, this is not correlated with exertion level, with a correlation value of r = −0.012.
The most highly correlated pair of datasets had a correlation value of 0.33.
We realized that two profiles are highly correlative (Fig. 7B, black), with a correlation value of 0.97.
These two have some degree of correlation, with a correlation value of r = −0.34.
These two have a very strong degree of correlation, with a correlation value of r = −0.89.
They use a correlation value equal to 0.975 for all admixtures based on the manufacturer recommendation.
Let and denote the mean and the standard deviation of a correlation value at the th lag, respectively.
For example, case 3 in Fig. 5 shows that the method produces a correlation value of 0.62 before the phenomenon even begins.
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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