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It is based on a multiple variance calculation.
Both formulations result in the definition of multiple variance components.
Groups were compared using the Kruskal-Wallis Anova and multiple variance analysis.
Recorded data were statistically analyzed using a multiple variance analysis with repeated measurements.
However, in multiple variance analysis, FM and WP were similar, and significantly different than RP and NP.
For instance, methods were reported that, to our knowledge, do not exist: e.g. "multiple variance analysis" or the "least squares difference" post-hoc test after ANOVA.
Similar(51)
Instead, multiple variances are determined for different frequency ranges resulting in a multidimensional feature vector.
Models were fitted with and without multiple variances, and comparisons were made with the Akaike Information Criterion (Akaike 1974) to select the optimal model.
We checked the performance of the results over multiple variance-covariance matrices ψ and report the results for two representative cases where the variance-covariance parameter matrices are multiples of ψ, which were estimated directly from the data: and The estimated biases and standard deviations of the fixed effects parameters are reported in Tables 6 and 7 respectively.
Individuals with WP and FM had no significant differences in the assessed domains in multiple variances analysis.
Data were compared by one-way ANOVA and significant differences obtained using the Tukey multiple variances post hoc test.
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