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An extensive multivariate analysis procedure for prediction of the blast fragmentation is presented.
In this research, an innovative multivariate analysis procedure for prediction of blast-induced ground vibration is presented.
The data were analyzed in accordance with the multivariate analysis procedure of SAS and, the treatment means of parametric and non-parametric data were compared by Bonferroni's test (P < 0.05) and, by Dunn's test (P < 0.05), respectively.
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Conventional research usually employs the multivariate analysis procedures.
Multivariate analysis procedures and a neural network methodology are used to predict mean particle size resulting from rock blast fragmentation.
The study used univariate, bivariate, and multivariate analysis procedures to analyze the data.
A multivariate analysis (MVA) procedure based on Bayesian statistics was used to cross-validate information obtained by different investigation methods, thus allowing the uncertainty or effort to be updated (reduced) when additional investigations are included in the analysis.
Factorial design is a multivariate data analysis procedure that can be used to optimize the manufacturing processes of lipid nanocarriers, providing valuable information and minimizing development time.
Variables that were univariately associated with kinesiophobia (p < 0.10) were selected for a multivariate analysis (step backward) procedure.
A multivariate analysis of variance procedure (MANOVA) was used to assess whether a significant difference remained in the overall assessment of sRAGE and S100A12 levels between the two groups.
These five conceptually based clusters were first analyzed by multivariate analysis of variance procedures, followed by univariate analyses of the individual variables composing each domain.
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