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Using a sample of 151 high-income girls in the United States, Brooks-Gunn et al., showed that mothers (85% had higher education studies) had a better correlation for assessing breast development than their daughters (r = 0.8 vs 0.7 respectively) [ 8].
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Pseudo-second-order kinetic model provided a better correlation for the experimental data.
Further, there appears to be better correlation for mutations with large changes in binding energy.
Because phenotype was ordinal categorical, we used Brier scores instead of Pearson's correlation coefficient for assessing prediction accuracy, because the Brier scores are bounded between 0 and 1, and values closer to zero imply better prediction accuracy.
Spearman's rank correlation was used for assessing correlations.
Spearman's correlation was used for assessing correlations between endocrinological markers and sperm counts.
We analyzed the strength of the correlations using Pearson's correlation coefficient, which is a standard, well-established method for assessing correlation.
Generally, we obtained better correlation coefficients for epitopes created by terminal modifications of LacNAc.
Sum-based normalization was applied to obtain better correlation coefficients for proven lectins (data not shown).
This fact necessitates the desire of enhancing such methods for better correlation with subjective quality assessment.
The least square regression equation and correlation coefficient were used for assessing the linearity.
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