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The coefficient of determination (R2 = 0.68), suggests that 68% of the variability in the data was explained by Eq. 2.
To determine how much variance in the data was explained by each of the variables, we compared the total variance between each of the models.
The percentage of variability in ants as explained by other sampling techniques was as follows: 60.0% of the variability in bait data was explained by pitfall trap data, while only 19.7% of the variability in pitfall trap data was explained by excavation data.
Moreover, it showed that 53% of the total variance in the data was explained by the differences between the NTERA-2 cell line and the rest of the populations, 36% of the variance was due to differences between MAPC and MSC-ADSC populations, and only 6% of the variance was due to differences between MSC and ADSC populations (Figure S1).
A total of 63.4 % of the variance in the data was explained by the measures and with a perfect model fit, this was expected to be 63.1%%.
This suggests that more of the variation in the data was explained by variation among the plants than by variation between leaves of the same plant.
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All quantitative data were explained by analyst software.
Next, we have fully examined how the focal mechanism data are explained by the estimated stress.
Research data are explained by the thermodynamic processes proceeding in model solution.
This means that 83% of the data are explained by the estimated regression equation.
This value expresses what fraction of the variance of the data is explained by the fitted trend line.
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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