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The factor matrix yielded five factors with eigenvalues ranging from 1.70 to 1.44.
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Factor analysis produced eigenvalues ranging from 3.12 to 0.32 with extracted item communalities ranging from 0.31 to 0.80.
For comparison, the individual factors had eigenvalues ranging from 11.7 (IP/CMC factor) to 1.7 (spine factor), compared with values not exceeding 1.1 for the higher order analysis.
This yielded coefficients of variation among the corresponding canonical eigenvalues ranging from 0 to 175%.
Eigenvalues ranged from 5.80 to 1.02.
Results revealed three factors with eigenvalues >1 ranging from 10.14 to 1.27.
The pattern matrix yielded six factors with eigenvalues greater than one (ranging from 5.28 to 1.01) and before the scree plot flatlined.
The initial PCA produced eleven factor components with eigenvalues above 1 (range: 1.022 -5.352), which explain a total of 68.67 per cent of the variance.
We also obtain a number of pointwise and Lp decay properties of the L2 eigenfunctions associated with eigenvalues E in the range 0 ⩽ E < N24.
Firstly, the relationship between eigenvalues of estimator and transfer function is built to analyze the suppression and amplification effect on input signals and obtain the eigenvalue range.
We consider the set of eigenvalues that deviate from the eigenvalue range of the random matrix as genuine correlation.
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