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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.
Statistical analyses were performed using either an unpaired independent t-test or a one-way multiple variance analysis (i.e. analysis of variance [ANOVA]).
The study variables related to treatment are shown on the table 2: - Multiple variance analysis techniques to evaluate the differences between pre- and post-treatment values in dependent variables for both groups.
Similar(54)
Individuals with WP and FM had no significant differences in the assessed domains in multiple variances analysis.
However, the multiple variances analysis showed that, for the majority of FIQ items, NP and RP were similar and statistically different than the groups WP and FM (which, in turn, where not different).
There have been two main quantitative genetic approaches used to clarify causes of observed variation in the human dentition: classical correlation analysis and multiple abstract variance analysis.
Continuous variables were compared with a one-way ANOVA followed by a post hoc Tukey's Multiple Comparison Test if variance analysis was significant or with a t-test analysis.
Multiple comparisons used the Kruskal Wallis variance analysis with Dunn s' multiple comparison test.
Related(14)
multiple variables analysis
multiple testing analysis
multiple comparision analysis
multiple model analysis
multiple resolution analysis
multiple correspondence analysis
multiple imputation analysis
multiple sequence analysis
multiple correlation analysis
multiple regression analysis
multiple variance calculation
multiple alignment analysis
multiple factor analysis
multiple variable analysis
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