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The existence of correlation between different measured variables was determined through Spearman correlation coefficient (rs).
The effect of L-NMMA on repeatedly measured variables was assessed by Friedman's two-way ANOVA.
Multiple regression analysis of the different measured variables was carried out to test the factors independently associated with maternal HOMA values.
To estimate uncertainty in the results, the standard error of each measured value (ΔA, ΔB) was used; the relationship of the final parameter to the measured variables was used to propagate standard errors.
However, there were few differences in patient characteristics across the volume groups and the unadjusted and adjusted measures were similar, suggesting that patient-level confounding (at least by measured variables) was small.
Statistical analysis of all measured variables was performed using SPSS for windows, Version 14.0.
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Normally distributed data are presented as means ± standard deviation, and differences of serially measured variables were analyzed using analysis of variance for repeated measurements with Bonferroni correction.
With this regard, the importance of hydrodynamic characteristics, either as manipulated or measured variables, are paramount.
Two output measured variables are usually considered for regulation: wheel deceleration and wheel longitudinal slip.
To build the complex network model, the process measured variables are represented as network nodes.
Such measured variables were statistically modeled by full factorial experimental design and response surface methodology.
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