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There are other methods for ranking variables.
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Different groups of subjects were compared by ANOVA for normally distributed continuous data, Fisher's exact test for categorical variables and the Cochran-Mantel-Haenszel test for ranked variables.
Differences between groups in baseline characteristics, safety, acceptability, and efficacy were tested using Fisher's exact test for yes/no variables and the Mann-Whitney U test for ranked variables.
The association between outcome and exposure variables was assessed using cross-tabulations, with Pearson's chi-square analyses (and Spearman's correlation for ranked variables); significance for all analyses was set at p < 0.05.
This is a test for both location and shape to test for differences between distributions of ranked variables.
Finally, the approaches for ranking of variables (SA based on coefficients of determination) and variance-based methods are carried out based on the surrogate model in order to quantify the global SA.
Notice that in our definition we used uppercase K for the rank variables whereas in Ref. [18] the authors used lowercase k. Figure 5 Recency-based individual mobility model.
The associations between the degree of severity of ADHD core features and the severity of specific ED symptoms were examined using Pearson's product-moment correlations (for continuous variables) and Spearman's rank correlations (for rank-ordered variables).
These data were compared using Fisher's exact test for binary data and the Mann-Whitney U test for counts and ranked variables.
This was especially true for the top ranked variables (bottom left corner).
Non-parametric test was conducted for a ranked variable such as the frequency of bronchodilator used during asthma attack.
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