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Furthermore, followed by the non-search feature selection strategy, optimized feature matrix was obtained through the procedure of mean analysis, variation coefficient analysis, cluster analysis and correlation analysis.
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When these taxa are excluded from the analysis, mean intraspecific variation drops to 0.23% (SE = 0.007; n = 1,964 comparisons in the analysis of full-length barcodes).
By means of analysis of variance the variation in measurements is partitioned into the potential sources of variation: observer differences, patient differences and random error.
The present analysis showed that mean intraspecific variation was 0.73% (SE = 0.033; n = 2,257 comparisons in the analysis of full-length barcodes) when all species of Bavarian geometrids were included, but this value was elevated by the inclusion of the 20 species with deep barcode divergences, some of which may represent species pairs overlooked by the current taxonomic system.
A summary of the statistical analysis for delay conditioning is given in Table 2. Mean and variation for all response parameters are given in Supplementary Table S1.
The statistical significance of the ratio of the mean square variation due to the regression and mean square residual error is tested using analysis of variance (ANOVA).
In a study by Hahne and colleagues [ 22] analysis of variation, k-means clustering and functional annotation were applied to transcriptome and proteome data from salt-stressed B. subtilis cells.
Mean greenness and variation in greenness were then classified into tertile groups for analysis based on rationale previously published elsewhere [ 35].
Analysis of variation at a single locus, gdh, provided a rapid means of identifying misclassified isolates and determining whether mixed cultures were present.
Mean coefficient of variation was 5% (20).
Results are mean with standard variation (SD).
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