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Nonparametric approaches for estimating optimal transformations of data were used to obtain the maximum correlation between observed variables.
Data processing Make the necessary transformations of data using an extraction, transformation, and loading (ETL) techniques between different data sources.
Since the response variable was not normally distributed, different transformations of data (logarithmic, square root, etc).
Furthermore, component equations relying on nonlinear transformations of data are nonadditive.
This is accomplished through the eigenvalue-decomposition of covariance matrixes and a series of linear transformations of data.
The assumptions of regression analyses – normality, linearity, and homoscedasticity – were examined through residual scatterplots, and transformations of data were conducted as needed.
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Unlike other methods such as Fourier transforms or wavelets, RQA requires no transformation of data and can be used for both linear and nonlinear systems.
The preprocessing and transformation of data.
First three of them after log-transformation of data were examined for bioequivalence.
AUC0 t, AUC0 ∝, and Cmax were tested for bioequivalence after log-transformation of data.
Absolute fidelity in any syntactic transformation of data from one format to another is very difficult to achieve.
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