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The results of the principal components analysis showed four principal components (PCs).
The first four principal components (PC) account for 49% of the total sample variation.
This model is used as a starting point in this paper and has four principal components.
Four principal components accounted for 81% of the variability in eelgrass cover.
D-optimal design was applied to the first four principal components derived in the PCA.
We choose four principal components to form the compression matrix, so the compression ratio of PCA is 0.03.
The result showed that the first four principal components (PCs) explained a total of 94 % variability of dataset.
The first four principal components (PCs) containing 99% from the total variance are presented in the Figure 5B.
The principal component regression analysis was performed on the shear strength and the scores of the first four principal components.
Four principal components (PCs) were extracted in PCA which explained more than 73% of the total variance in water quality.
Four principal components were used as observed factors for each country, and the ACM three-step procedure followed.5 The resulting affine models fit the yields quite satisfactorily.
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