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As the water level, we use 10% of the maximum variance from all the three station components.
However, the maximum variance would be as large as 78.21, which is noticeably larger than the maximum variance from simple kriging.
First, this factor analysis is applied to derive maximum variance from the data set with individual components; hence, it serves to decrease a greater number of variables into a fewer number of components (Tabachnick and Fidell 1996).
The objective of using PCA and of this study is to extract maximum variance from the data set, to reduce a large number of variables down to a smaller number of components, and to initiate factorial analysis to reveal the nature of component factors (Tabachnick & Fidell 2012).
The linear combination that extracts the maximum variance from the data is termed the principal component.
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PCA seeks a linear combination of variables so that the maximum variance is extracted from the variables.
Until the variances of the points are equal to the maximum variance of intervals derived from the last step, this decreasing procedure continues.
With the help of principal component analysis and the rotation method of maximum variance, 7 factors were extracted from 31 items and the 7 factors' eigenvalues were over 1, which could explain 61.956% of the total variance.
Sensitivity coefficients calculated when the variance of AP-1 was less then 10% the maximum variance of AP-1 wexcludeduded from further analysis to avoid numerical oddities, as shown in the Additional file 1, Figure S2.
Two features were calculated on the bone-contour points: (i) the direction of maximum variance of the distribution, and (ii) the farthest point from the field of view border among the projections of the bone-contour point along the maximum variance axis.
The criteria for determining the best hypothesized arrangement were a significant departure from a random distribution and the maximum variance among groups (ΦCT).
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