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Using weighted mean correlation is recommended especially for cases when the individual correlations are not high.
The majority of the studies (45%) were conducted in Hong Kong, followed by US (18%) and Canada (14%). Figure 2 shows that the overrall mean correlation is moderate, r = .46, 95% CI [.42,.49] and significant, z (27) = 24.50, p < .01.01
The estimated true mean correlation is computed as an average of observed correlations, as previously mentioned.
Consequently, we performed a one side unpaired t-test to evaluate whether the mean correlation is larger in the first case.
The difference in the mean correlation is highly significant (p < 0.001, two sample t tests) for each point (all values of d) on both curves.
In addition, the analysis of RNA levels estimated by RT-PCR showed that the mean correlation is higher than the array based platforms, albeit the number of samples in the analysis is also small.
Similar(53)
The mean correlation was 0.56, indicating that there was no multicollinearity, which could have compromised the regression results.
Within our time resolution, the mean correlation was the highest at zero lag time.
For the shuffled data, the overall mean correlation was 0.01 ± 0.01.
The variants with the highest mean correlation were QMEANfamily (082) with a mean correlation of 0.778 and MULTICOM-REFINE (013) with a mean correlation of 0.768.
In addition, the correlation between 7 different genes and the corresponding protein levels was calculated (Additional file 8), where the mean correlation was 0.58.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com