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The intergroup comparison will be performed by simple variance analysis (one-way ANOVA) for data with normal distribution and the ANOVA Kruskal-Wallis for non-normal distribution.
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A simulation example shows that the statistical properties also in this case are much better than predicted by average variance analysis if Δ< 0. For this simple example, it is hence possible to monitor the quality of the estimate by studying the sign of Δ. Traditional variance analysis only considers the average effects and hence misses this two mode (good or bad) situation.
Demographic characteristics, arterial indices and the number of CFUs among the three groups were compared by simple analysis of variance (ANOVA) with post hoc comparisons by multiple t tests.
This was further demonstrated by variance analysis (Table 2(c)).
Data (mean ± standard deviation) were tested by variance analysis.
The results were analyzed by ANOVA (one-way variance analysis).
Correlations among variables were tested by simple regression analysis.
r, correlation coefficient by simple regression analysis.
Statistical analysis was performed using simple variance test.
Relationships between variables were determined by simple linear regression analysis.
Scatter plots were evaluated by simple linear regression analysis.
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