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Using the principal component extraction method with an eigenvalue > 1, the first-order factor analysis extracted six factors instead of the seven reported in the original study by Hare and Davis (1996) [ 24].
Principal component analysis extracted collective motions.
Principal component analysis extracted two components, which explained 64.24 % of the total variance.
R-mode analysis extracted two components with eigenvalues >1, which explained 64.24 % of the total variance.
Results: Principal component factor analysis extracted 2 food intake patterns (Factor I and II).
The line profile EDS analysis extracted from EDS mapping clearly shows the distribution of oxygen in Figure3c.
The PCA analysis extracted two factors accounting for 76% of the total variance among the considered species (48% and 28% for the 1st and the 2nd factor, respectively).
Redundancy analysis extracted a main axis explaining 67% of the variation of the microbiological soil properties, which was interpreted as an environmental gradient of soil fertility.
Principal component analysis extracted 6 PCs which explain 81% of the total variance of the original data matrix and heavily by EC, TDS, TH, Ca2+, Mg2+, Na+, and Cl−.
In this analysis extracted areca nut (control sample) and dye samples formed by coupling it with diazotised amines were analysed to confirm the chemical modification of areca nut extract as natural dye.
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As no comparable studies were identified, and as quantitative data could not be statistically combined for a meta-analysis, extracted data were synthesised into a narrative summary.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com