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The main dimension explains 39.75% of the data inertia.
Factor 1 explains 40.74 % of the total variance.
An analytical model, which explains spring in a partition wall, is presented.
The GLM model explains 64.75% of the total variation in Kfs.
The second LDA included the RQA toolbox parameters, where LD1 explains 73.42 % of the observed variance and LD2 explains 14.13 % (Table 6).
The first PC (PC1) explains 52.85 % of the variation described by the original set of variables, whereas the second PC (PC2) explains 37.24 %.
The first three PCs accounted for more than 75% of the variance in the TRS, where PC1 explains 49.87% and PC2 explains 14.12% of the variance.
The first principal component explains 30 40% of the variance; the first and second principal components explain 55 60%.
It explains 20.56% of the variance (Eigenvalue: 2.44).
Relative Warp 1 explains 56.01% of the total shape variation; RW2 explains 22.86%.
This factor explained 46.45 % of the variance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com