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The National Mental Health Association is working to quantify "unmet need" in 13 states and is struggling with variances in data collection and systems, said Maril Olson, the association's director of child welfare.
Considering wild-derived and lab-derived strains independently, principal component analyses (PCA, an unsupervised linear feature extraction method that discovers the directions of maximal variances in data) found highly significant correlations between non-synonymous SNP distribution and phylogeny within lab strain V2Rs (Two-way ANOVA, variance by V2R clade F12,858 = 17.99, P < 0.0001).
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Homogeneity-of-variance in data was determined by Levene test, and formal distribution pattern of the data was confirmed by Kolmogorov-Smirnov test and P-P Plots.
PCR and PLS methods involve the decomposition of the experimental data, such as spectrofluorimetric data in this case, into systematic variations (principal components or factors) that explain the observed variance in data.
PCA helps to explain the variance in data and is a common technique for dimensionality reduction in high dimensional data.
Homogeneity-of-variance in data was determined by Levene test, and formal distribution pattern of the data was confirmed by Kolmogorov-Smirnov test and P-P Plots [ 82].
The PCA was conducted using CLUSTER, so that the clusters were ordered and chosen to maximally explain the remaining variance in data vectors [ 1].
Variance components are derived from statistical random effects models [ 8], by which the total variance in data is partitioned into estimated variance components associated with different random factors, i.e. sources of variability, in the model.
This allows the segmentation algorithm to overcome minor variances in the data that could arise from noise in the data.
We will take any kind of error model — Gaussian, Poisson, even the (local) variances in the data themselves.
This analysis resulted in the explanation of 71.53 79.26 of variances in the data.
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