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Correlation between pairs of continuous measures was calculated using the Spearmans correlation coefficient.
Spearman rank correlation was used to assess the relationship between pairs of continuous variables.
Correlations between pairs of continuous variables were calculated using Spearman's correlation coefficients.
Associations between pairs of continuous variables were tested for statistical significance using Spearman's correlation coefficient.
The significance of differences between pairs of continuous variables was evaluated by the Student's t-test or the Wilcoxon test, as appropriate.
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Comparisons between pairs of groups for continuous variables were performed using Mann–Whitney U test.
The minimum diameter of the resident actinomycete at the time the zoi of the intruder actinomycete was scored, and the relative genetic distance between pairs of actinomycetes were inserted as continuous covariates.
Chi-square tests were used for the pairs of categorical variables; nonparametric Kruskal-Wallis tests were used for the pairs of categorical and continuous variables, while Spearman rank correlation tests were used for the pairs of continuous variables.
We performed pair-wise logistic regression of each variable to the outcome and determined the Pearson correlation coefficient between each pair of continuous variables.
Traditional approaches infer local recombination rates by calculating the ratio of genetic to physical distance between pairs of markers mapping on the same continuous genome sequence.
Associations between pairs of variables were assessed using Pearson correlation for continuous variables, Spearman rank correlation where one or both variables were ordinal, or tetrachoric correlation for two binary variables.
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