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For this reason, the interpretation of parametric designs in the presence of multi-collinearity is problematic.
A careful interpretation of parametric perturbations used in the PSA is presented here to explain the issue of using this analysis in inferring dynamics.
The current results support a responder definition of one point on the FSS score; however, cumulative response distributions at critical time points are recommended to aid interpretation of parametric results.
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However, the analysis and interpretation of multi-parametric cellular descriptors is a more challenging task.
One interpretation of non-parametric bootstrapping that is compatible with Bayesian inference was also proposed [[ 37], page 7090]: "In a Bayesian sense, can be thought of as reasonable assessments of error".
This paper provides a practical guide to the application and interpretation of this non-parametric item response theory method in empirical research with health and well-being questionnaires.
However, the appropriate interpretation of inconsistent results across parametric and non-parametric analyses is not entirely clear.
Next, a fuzzy system based on logical interpretation of if then rules and with parametric conclusions is presented.
However, the Cox model has a more straightforward interpretation of covariate effects than non-parametric multi-state models and allows tests to be performed to determine if the effect of a covariate is significantly different from zero.
This is probably the appropriate interpretation, since rim volumes, unlike cup volumes, were fairly normally distributed, allowing the use of parametric statistics.
The interpretation of the PSA coefficients of dynamical systems should take into account the persistent nature of parametric perturbations involved in the derivation of this analysis.
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