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In contrast, the multivariate approach of PDC is suited to characterize solely direct dependencies of two signals under study.
As mentioned before, MI measures the information that X and Y share by measuring how much knowing one of these variables will reduce the uncertainty of the other and reflects the statistical dependencies of two variables.
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Here we utilized genome-wide loss-of-function CRISPR screens to reveal genetic dependencies of eight PEL cell lines.
The pressure volume temperature (PVT) dependencies of four molten polystyrenes (PS) were determined at T=450 530 K, and P=0.1 190 MPa.
By showing the EIS dependencies of three different temperatures, five SOCs and five cells with different cathode chemistries towards relaxation behavior, the importance of acknowledging relaxation time as an inner state is emphasized.
As a result, we obtained the diurnal dependencies of five MSTID characteristics for each date under the period of consideration.
Figure 3 shows, for example, diurnal and seasonal dependencies of four model parameters: minimum range, skip distance, elevation angle, and ratio between the total range to ground scatter and the range to the ionospheric reflection point (the ray trajectory apogee).
Pearson's linear correlation coefficients were used to measure the dependency of two variables.
The CD, derived based on the multivariate T2 statistic, is used to measure the causal dependency of two variables.
Canonical correlation analysis (CCA) is a classical tool that explores the dependency of two multivariate random variables and extracts canonical pairs of highly correlated linear combinations.
In this article a statistical dependency of two parameters of random load states (the amplitude and the mean of load cycles) is estimated by means of the multivariate p.d.f. of rainflow load cycles that are represented as vectors S=(Sa,Sm).
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