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Variable cluster analysis not only identifies the interrelationships between variables, but also overcomes the problem of interpretation related to principal component analysis.
The problem of multivariate alarm analysis and rationalization is complex and important in the area of smart alarm management due to the interrelationships between variables.
Analysis of the data produced reveals the interrelationships between variables relevant to the management of risk reduction, including the costs of safety measures of risk reduction, the occurrence of accidents and their cost, the material operating budget for the project and the health and safety plan budget.
Interrelationships between variables were analysed using crosstabulations, the Chi-square statistic and bivariate logistic regression.
There were many interrelationships between variables; not all are reported here.
Interrelationships between variables were assessed using contingency table analysis with the χ test for trend as appropriate.
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The identification of system variables and the interrelationship between variables are of prime importance to achieve the exact structural model.
Cluster analysis (CA) and principal component analysis (PCA) were employed to evaluate the water quality and the interrelationship between variables.
Identification of vital and significant variables and establishment of interrelationship between variables is very important for accurate model development using ISM.
Interrelationship between variables was assessed using contingency tables; Phi analyses for dichotomous variables and Spearman rank order correlation for ranked data.
The interrelationship between variable NAT activities and the toxicity or carcinogenicity of environmental AAs has been reported (Badawi et al. 1995; Gemignani et al. 2007; Hein 1988; Minchin et al. 1993).
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