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Neurocognitive measures reflect a meaningful amount of shared variance whereas the neurophysiological measures reflect largely unique contributions as endophenotypes for schizophrenia.
Together, the fixed and the random factors explain 78% of the variance, whereas the fixed factors alone explain 10%.
The quantitative data were analyzed through descriptive statistics followed by univariate and multivariate analyses of variance whereas the qualitative data were examined through descriptive analysis.
In general, the background can be distributed over the entire image exhibiting a high spatial variance, whereas the foreground objects are generally more compact.
Unidimensionality analysis revealed that the first factor of the victim QBO scale explained 26.27 % of the variance, whereas the first factor of the bully QBO scale explained 31.05 % of the variance.
To estimate the percent of variance explained by each of the psoriasis risk alleles, a previously described liability threshold model was used [26], [27].Our data show that HLA-C, IL12B and TNIP1/ANXA6 were estimated to account for 6.7%, 1.3% and 1.0% of the genetic variance, whereas the remaining loci each accounted for less than 1% of the genetic variance (Table 4).
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The three-factor solution (indicated both by the Kaiser Rule plus Parallel Analysis and Scree Plot) accounted for 34.45% of the total variance, whereas in the international sample the same structure was responsible for 32.74%.
The SNPs associated with NCLB explained in a simultaneous fit 9.38 and 28.94% of the genetic variance, whereas those associated with NCLB FT explained 0 and 23.20% of the genetic variance in the SSS and Iodent pool, respectively (Table 3).
All the QTL found for Z1 explained 11% of the phenotypic variance whereas those found for Z2 explained 53%.
For the differentially expressed genes, within group variance should be smaller than between group variance, whereas for the genes not differentially expressed, the respective MSQbetween and MSQwithin values should show no great difference.
In the case of randomized designs, the purpose of ANCOVA is to reduce error variance, whereas in the case of non-randomized designs (or of analyses involving substantial pre-existing group differences), ANCOVA is used to adjust the post-test means for pre-test differences among groups (Dimitrov et al. 2003).
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