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The credibility of associations between multiple variables was assessed by false discovery rates (FDR, [ 34, 35]).
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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.
Statistical evaluation of multiple variables was performed.
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.
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.
Correlation matrix is used for determining the dependence between multiple variables at the same time.
SEM is used to explain the relationship between multiple variables and concepts.
Additionally, the mutual influence actually exists between multiple variables.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com