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Analysis of variance was performed for more than two groups, the Kruskal-Wallis test was used for variance inequality or lack of variance homogeneity.
Test of F in the ANOVA analysis is used for evaluating model variance in residual variance.
It is shown that consistency holds when incorrect values are used for the variance components.
The name (w_{kal}) will be used for the variance of the process noise chosen.
Two values were used for the variance, the same two values as used in experiment one (0.05 and 0.2).
SPSS 17.0 statistical software was used for data variance analysis.
The residual maximum likelihood technique was used for estimating variance components.
Kolmogorov-Smirnov tests were used to describe the data distribution; the one-way ANOVA test was used for analyzing variance.
For completeness of the results, we also provide all effects and AIC values adjusted for small samples (AICc) of each model used for variance partitioning and a comparison against a null model (i.e. a model without predictors) (Supplementary Table 3c, Supplementary Data 1a-c).
All analyses have to be repeated for each plausible value and replication techniques have to be used for variance estimation (e.g., Jackknife 2 in PIRLS; see Foy and Kennedy 2008).
This situation arises when various physical processes, such as neutron transport, can generate additional particles during the time step, or when particle splitting is used for variance reduction.
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