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Using the Principal Component Analysis, they grouped these variables into three main components, explaining 75%% of the total variance of the 15 variables.
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The two main components explain 50.03% and 13.28% of variance.
Results of the principal component analysis showed that the two main components explained between 74% (dog) and 87% (cattle) of the total inertia depending on the species considered.
After calculation of pairwise IBS genetic distances between all individuals, we derived genomic components, summarizing the structure of the data into main genomic components explaining the genetic variation (Fig. 2).
Three main PCA components explained 56% of the variance in gene expression and mapped the test drugs in three-dimensional space according to their molecular profiles.
As depicted in Table 5, EFA extracted the following seven main components which explained 64%% of the total variance in the sample.
All the variables can be classified according to the main components which explained 66.13% of the information (51.61% for F1 and 14.52% for F2).
Principal component analysis identified three main components that explain 86% of the total variance.
A Principal Component Analysis (PCA, a mathematical representation of the dataset diversity [ 25]), performed on the transcriptome dataset, identified two main components that explain 29.5% and 9.6% of the variance.
This analysis revealed two main components which together explain approximately 60% of the variance.
These costs are based on (1), which have three main components as previously explained: energy consumption, machine market price, and collocation cost.
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CEO of Professional Science Editing for Scientists @ prosciediting.com