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These explanations are useful for finding relationships between groups of variables and treatments.
Simple correlations were performed for numerical data whereas canonical correlations were used to analyse correlations between groups of variables.
The aim of A/B tests is to detect statistically important differences and regularities between groups of variables to reveal improvements.
More data were lost in the control group; this could be one reason for the difference between groups of variables.
Matrix correlation methods [ 3, 26] have also been used to quantify the association between groups of variables and one or more PCs.
This method, in addition to identifying associations between groups of variables, allows the same variable to become part of several factors.
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Differences between groups of categorical variables were evaluated by χ test and that of continuous variables were evaluated by the Kruskal-Wallis test.
A unique group method of data handling (GMDH) multilayer algorithm is used to deduce the relationship between groups of input variables and output functions.
Our goal is to develop computational methods to explore higher-order relationships between groups of predictor variables that discriminate between cases and controls.
Differences between groups of continuous variables were determined by Mann–Whitney U or Kruskal Wallis ANOVA.
Differences between groups of categoric variables were compared using the χ test.
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