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Depending on the number of input variables, it creates principal components (PC's) representing the maximum variability in information data and its variance in descending order (PC1, PC2, PC3, etc).. Usually, the first two components (PC1 and PC2) are found to be the best means to carry out the classification of input variables.
Maximum variability coefficient of planting depth is 8.40%.
The maximum variability of rainfall is recorded during the months of July and August.
The average moisture at which the maximum variability occurred depended on soil hydraulic properties and vegetation.
The X-axis represents components that have maximum variability and y-axis represents components having second highest variability.
PCs actually take the cloud of data points and rotate it such a way that maximum variability is visible (Fathy et al. 2012).
At first the data were preprocessed and then PCA were applied to determine the maximum variability directions of elements in the area.
This sequence was altered in 3 steps to incorporate regions of maximum variability, identified as significant changes in the PCPs, characteristic of DENV-4 strains.
The results emphasize that, by varying the OMI values (Loss OMI− and Loss OMI+), Rovigo Province records a maximum variability (± 14%) and, conversely, a minimum value is recorded for Bologna Province (± 9%).
Accounting for the extreme values of both Cr and OMI parameters, the Loss min and Loss max have the maximum variability for Rovigo Province (− 43 to + 53%), whereas the lowest difference occurs in Bologna Province (− 36 to + 42%).
Pogoreltsev et al. (2014) found the maximum variability of QBO winds at altitudes near 30 km (pressure levels 10 20 hPa) and determined the westerly and easterly wind zones at those heights.
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