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This is computed using a Student's t-distribution for a transformation of the correlation.
The transformation of the correlation patterns of the regional consumer markets mainly revolves around three core correlation modes and each transformation needs to undergo 4 non-core modes.
There also exist generalized versions of cross-correlation that calculate the cross-correlation for each assumed geometric transformation of the correlation windows [6, 7].
Pearson's correlation coefficient was calculated for differences between imaging techniques, and Fisher's Z transformation of the correlation coefficient was employed using SAS software (SAS Institute, Cary, NC, USA).
To perform the calculations, the Fisher transformation of the correlation was used: z = 0.5 ln[(1 + r)/(1 - r)] and z having a standard error SE z) = 1/√(n - 3), where r is the correlation and n is the sample size.
A weighted adjacency matrix is a continuous transformation of the correlation matrix.
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This difference is statistically significant in height and BMI, based on a Fisher z-transformation of the correlation coefficient (see Materials and Methods).
Furthermore, the performance achieved by the meta-model is often statistically significantly better compared with the performance of its components, where statistical significance is tested using a Fisher z-transformation of the correlation coefficient.
For brevity, we denote the Fisher transformations of the correlation coefficients cor(A, B) and cor(M, B| A) by Z(A, B) = Z Fisher (cor(A, B)) and Z M, B| A) = Z Fisher (cor(M, B| A)), respectively.
Then, the power spectrum latent in the actual noise or vibration signals with no amplitude limitations can be determined through Fourier transformation of the linear correlation function based on the well known Wiener-Khintchine theorem.
The power calculation was based on Fisher's ZR transformation of the intraclass correlation coefficient.
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