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Considering Table 3, the seven main effects only account for 5.7% of the explained variance, whereas all 64 effects account for 20.4% of the explained variance.
Considering Table 5, the main effects account for 10% of the explained variance, whereas all 64 effects account for 25.3% of the explained variance.
The r 2 is the ratio of the explained variation to the total variation.
Soil and nutrient deposition accounted for respectively 2.2% and 1.4% of the explained variance.
The proportion of the explained variance is lowest for the naïve and traditional classifications.
All of the explained properties are desirable properties important for use in proton exchange membrane applications.
The LST accounted for 22.9% of the explained variability in NDVI.
A second-degree polynomial model is developed which accounts for an excellent proportion of the explained variation (R2= 97.7%).
Principal component analysis (PCA) allowed a clear differentiation of the studied cigarette types (PC1 describing 94% of the explained variance).
The best PARAFAC model was obtained with 3 components, having 55% core consistency values and 98% of the explained variance.
For example, the seven main effects of the ANOVA analysis displayed in Table 2 together only account for 7.3% of the explained variance, whereas all 64 effects together account for 33.2% of the explained variance.
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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