Additionally, for better explanation of the data, a General Linear Model (GLM) using a second-order model was applied to test and estimate the effect of musical training and the interaction with the conditional probability on EANm responses.
Using the ML mode and leaving all rate parameters between states unconstrained, we found that a model in which flight and small introns were associated was a slightly better explanation of the data than a model in which they were independent in two of three data sets (P = 0.09 in data sets A and B and P = 0.29 in data set C, χ test).
To do this, the Shimodaira-Hasegawa or Approximately Unbiased tests [ 52, 53], which hold the null hypothesis that all the trees tested are equally good explanations of the data (and the H1 hypothesis that some trees are better explanation of the data), can be used [ 48].
This study demonstrated that RTD modeling with reaction kinetics of suspended solids during storm events produces a better explanation of outflow data than possible with steady, plug-flow models.
Our electrophilic activation hypothesis offers a much better explanation of this data.
At 240 K and above, by contrast, multiple conformations provide a better explanation of the data.
For, alternative factor models may provide a better explanation of the data than the WHO 4- and 6-domain models.
However, these large studies did not investigate the possibility that alternative factor models may provide a better explanation of the data.
The goodness of fit of the reduced models was compared to the full model to assess whether they represented a better explanation of the data using the likelihood ratio χ test and the Akaike Information Criterion AICC).
Although there are many reports of the 4- and 6-domain models [ 2, 4], these studies did not investigate the possibility that alternative factor models may provide a better explanation of the data.
