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When there are no missing values to impute, the calculated score is the same as the known "true" score thus the scores correlate perfectly (spearman = 1.0) Figure 1 graphically displays the correlation between the predicted values and the observed scores when the probability of missing data is increased to 20%.
Panel a displays the correlation between procrastination and regional homogeneity (ReHo) values.
The graph displays the correlation between experimental (pK_a) values, and the values predicted by one of the QSPR models developed in this study.
The upper panel displays the correlation between Na and Ne that is shown as a solid line for 96 100 km, while the dashed line represents the temporal variation for Fe and Ne in the same altitude range.
Additional file 3: Displays the correlation between changes in HIT-6 score (x axis) and change in individual 36-Item Short Form Health Survey domains (y axis) during therapeutic diet (stage 2).
Figure 2 displays the correlation between POSTN and ITGB5 in all samples (Spearman r = 0.472, P < 0.001).
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Table 2 displays the correlations between composite-scales of teaching qualities of faculty and their being seen as role models.
Table 1 displays the correlations between the SCARED and the YSR for the self-report version.
Table 3 displays the correlations between the SHAPS and the other measures.
Table 3 displays the correlations between county incidence and mortality rates and health factor groupings.
Last Friday the Washington Post published a piece displaying the correlation between the Trump vote and white mortality rates, which is as close as a series of scatter plots have ever taken us to perfect poetry: as the demographics of the country shift, Trump's rise mirrors the death of the white American male, making its animating rage less an advent than a valedictory party.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com