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The classical LDA approach involves calculating the between-class scatter matrix by computing the covariance matrix of each class data center.
The first step of the approach involves calculating the loading and error variance for the single indicator of the latent product using measurement model parameter estimates.
The approach involves calculating the expected value of the spectral response of an equivalent linear system where the equivalent natural frequency is assumed to be a random variable.
The first approach uses the short-period signals, usually for spectral analysis and qualitative descriptions; the second approach involves calculating source time functions of the landslide using inversions of long-period seismic records (Ekström and Stark 2013; Hibert et al. 2014, 2015; Lin et al. 2010; Yamada et al. 2013; Zhao et al. 2015).
A commonly reported alternative approach involves calculating the percentage of cases for which there is 'agreement' between measures within a chosen 'margin of error'.
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The first approach involved calculating a variation of the robust Z statistic [ 32] as shown in equation 1: (1) Robust Z j = Med β j CAT − β j P & P 0.74 I Q R β j CAT − β j P & P where Med is the median and IQR is the interquartile range.
The most promising approach so far involves calculating the ratio of freely circulating P.S.A. to the total P.S.A. level.
Sensitivity analysis using the bounding approach was employed, and this involves calculating upper and lower bounds, using the Wilcoxon signed rank test.
A scenario sensitivity analysis method was applied in this study, which involves calculating different scenarios, to analyse the influences of input parameters (soil carbon accumulation) or methodological choices (allocation approach) on either the LCIA output results or rankings [ 71].
Traditional weaving involves calculating complicated systems of colour and design.
That involves calculating what's called a "capitalization rate".
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