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The anchor samples were also shuffled to see if there was any association between the random samples.
These figures demonstrate the significant variability between the random samples for the different dependent measures.
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No significant difference (p = 0.6; χ test) in distribution of practice types was observed between the random sample of GP practices and the GP practices that returned the questionnaire (50% solo and 50% duo or group practices).
However, a very low frequency of comparisons between the random sample datasets were observed at this point suggesting that a high number of European H5N2/H5N3 that are sequenced and uploaded to GenBank are also reported as reassortant.
Regarding the previous point, we have further analyzed the source of these differences in performance between SMOTE and the random sampling techniques.
In addition, there was no sex or ZIP code distribution difference between the source population, the random sample, and the CoLaus participants.
A very similar pattern was observed for the monocot-specific CNSs (supplementary fig. S5 B, Supplementary Material online) again indicating a clear nucleosome positioning in and around CNSs. Similarly a high statistical significance (P = 0) was observed between the CNSs and the random sample with same AT content.
An overlap between the case set and the random sample is a design feature of a case-cohort study.
The Wilcoxon rank sum tests were used to compare the distribution of correlation coefficients (of co-expressed genes) between highly interacting gene pairs and the random sampled gene pairs that were at least 100 kb from each other on the genome sequence.
There was no statistical significant difference regarding nasal NO concentration between the random population sample (114 ppb) and the "symptomatic sample" (116 ppb).
Significance testing using permutations was then carried out to determine the overlap between six random samples of 20,000 values (the number of correlations) from a total of 1,236,087 values (the total number of probes).
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