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Multivariate statistics are commonly used to compare individual land use types to control the dynamics of streamflow or sediment yields.
The distributions of each patient variable and multivariate statistics are shown in Table 4.
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No results of univariate ANOVA were reported if the multivariate statistics were not found statistically significant (Objective 1).
Design values are often based on univariate statistical analysis, while actually multivariate statistics is more appropriate for modeling the whole structure.
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.
Because of the large number of reflectance spectra in the image cube, multivariate statistics were used to reduce the dimensionality of the data.
In this study, GCMS technique and multivariate statistics were applied to profile the hepatopancreas and approached for metabolic marker when Litopenaeus vannamei were infected by WSSV.
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