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No results of univariate ANOVA were reported if the multivariate statistics were not found statistically significant (Objective 1).
Multivariate statistics were performed on ambient data using R statistical software (R development core team, Vienna, Austria).
Univariate and multivariate statistics were considered.
For this purpose, multivariate statistics were applied on the basis of data gathered with the Design of Experiment (DoE) methodology.
Multivariate statistics were then used to assess the influence of landscapes, tree hosts and nitidulid beetle associates as well as isolation by distance on fungal community structure.
Multivariate statistics were used to extract the main driving forces for changes in vegetation patterns out of a complex landscape ecological data set.
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Design values are often based on univariate statistical analysis, while actually multivariate statistics is more appropriate for modeling the whole structure.
Multivariate statistics are commonly used to compare individual land use types to control the dynamics of streamflow or sediment yields.
Multivariate statistics was performed on integrative proteomic fingerprints using principal component analysis.
The distributions of each patient variable and multivariate statistics are shown in Table 4.
A comprehensive quantitative morphological analysis using multivariate statistics is still not available for the A. lyrata complex.
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