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In addition to the raw data correlations, we performed correlations using the % year-to-year change for each of the datasets (difference model [4]), a total of 307 comparisons.
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In all three datasets, differences between the scores of first authors from different regions were statistically significant, even after application of the Bonferroni correction for multiple hypothesis testing (see Table 2).
In analyses of individual datasets, differences were observed by tumor histology.
This experiment did, however, suggest: (i) it is not necessary to expect a specific category in detection; (ii) dataset difference impacts performance; (iii) a system relying on such an approach may obtain worse results.
Multiple imputation of non-respondents to question about recent dengue cases reconciled 10 datasets (1000 iterations each) to generate almost identical data to those from the original dataset (difference −0.012, 95% confidence interval −0.020 to −0.004).
In the Twenty-07 Study (1990 dataset), differences between the baseline, age 18-19 responders and the age 18-19 weightedatasetet were all small and non-significant.
To evaluate the impact of the SNP dataset differences on genome-wide scans for signatures of natural selection, we performed (iii) sliding-window analyses (100 kb windows, 25 kb step size) to identify signals of putative selective sweeps based on population differentiation.
In BIRCH datasets, the difference is much more visible.
In comparing and contrasting the two datasets a difference factor of approximately-thirty occurs numerous times.
Even so, in all datasets the difference was minor, representing less than 1-1.5 inems in the index.
MIDs are often estimated using longitudinal datasets, and difference scores based on changes over time were not available in this dataset, which was cross-sectional.
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CEO of Professional Science Editing for Scientists @ prosciediting.com