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The optimal properties of information-theoretic terms in assessing variability and correlation between multiple variables are familiar to the machine learning and statistics communities but perhaps not well known in fields of epidemiology and medical statistics.
Individual variables and relationships between multiple variables are displayed in multiple linked views using user-selected box plots, histograms, scatter-plots, time series, parallel coordinates, and so forth.
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The credibility of associations between multiple variables was assessed by false discovery rates (FDR, [ 34, 35]).
As a first step, the relationships between these multiple variables are investigated using Spearman's Rank Order Correlation (rho), which can describe the strength and direction of relationships.
Rather than changing only one variable at a time, multiple variables are changed between experimental runs in order to isolate interaction effects between control parameters.
Multiple variables were analyzed between two groups.
Statistical evaluation of multiple variables was performed.
Meat quality is a function of the interplay between multiple variables and is of ongoing concern to pig producers, meat processors retailers and consumers alike [ 11].
Due to the limited number of children, statistical analyses uncovering more complex interactions between multiple variables could not be computed.
Relationships between sets of multiple dependent variables are analyzed with CCA (Darlington et al. 1973; Hair et al. 1986; Levine, 1977; Tucker et al. 1980).
Logistic regression was used to estimate the association between multiple variables and each of the surgery types.
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