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The volatiles that accounted for maximum variance in the data set are given more weight or loading.
PC1 demonstrates the maximum variance in the data and the second principal component (PC2) illustrates the largest residual variance along a direction orthogonal to PC1 and so forth.
To do so, PCA considers the maximum variance in the dataset, whereas MAD considers maximum autocorrelation, since it takes into account the maximum variance of the difference images.
The variance is highest around the perimeter where there are insufficient sensors to establish good spatial functions: the maximum variance in these regions exceeds 1,800, but the scale has been modified to show greater resolution in the central regions.
Briefly, the aim is to determine the direction of maximum variance in the space of data points.
Used to find the vector in X that explains the maximum variance in Y in linear regression methods.
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The first principal component (t[1]) best approximates the data in a least squares sense and represents the maximum variance direction in the data.
The first component (component of maximum variance, PC1 in Fig. 4B) clearly delineates a differentiation axis, with all differentiation stages clustered in chronological order from left to right.
Principal component 1 and 2 is plotted in the x and y axis which has the maximum variance is shown in Figure 6.
The mean, minimum, maximum, variance, and range in number of repeats across individuals were calculated from the repeat number dataset.
The maximum variance is found in the eastern Pacific along the equator and along through the South American coast, indicating that the most dominant SST variability is trapped along the eastern equatorial and coastal regions.
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