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At last, it takes 6 logistics parks as an example to investigate the evaluation system based on statistical analysis software SPSS and main component analysis.
A factor analysis for all items of the ECQ was conducted using main component analysis with varimax rotation.
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This new analysis also produced a new panel in Figure 2. The main Principal Component Analysis (PCA) is now verified using two different non-linear factorization techniques: Isomapping and Local Linear Embedding.
Factors were extracted after main components analysis followed by an oblique rotation.
Global main effects principal component analysis was performed as previously described (Box et al., 2005).
Figure 1 figure supplement 1 provides a global main effects principal component analysis for all GF and SM used in this second screen.
Consistent with this observation, a global main effects principal component analysis of all GF and SM revealed that CHR, WNT, Rspondin and FGF exerted the most potent effects on GFP expression.
DOI: http://dx.doi.org/10.7554/eLife.08413.003 10.7554/eLiFigure1 figureigure 1—figure supplement 1. Global main effects principal component analysis of GF and SM ACME screen demonstrates that WNT and FGF agonists exert positive effects on T-GFP expression.
The b i -axis may be interpreted in the sense of main axis from principal component analysis.
After the scale has been set in two main categories, a Principal Component Analysis was performed based on the CCT assumptions, using the tetrachoric correlation among the items of BISOC.
According to correlation and principal component analysis, main ES tradeoffs among ES availability observed at two spatial scales were represented by crop production vs. the other evaluated ES types (OES), and in contrast with our prediction, their strength was not higher for the fine- than for the coarse-scale (relatively large and internally complex observation units).
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