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To reduce multicolinearity between variables, we calculated the Spearman's correlation coefficients (rs).
To examine the potential relationships between variables, we calculated Pearson's correlation coefficients (for parametric data) and Spearman's rank correlations (for non-parametric data).
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To examine collinearity between explanatory variables, we calculated the variance inflation factor (VIF) for each variable, and the mean value of all VIFs.
To investigate possible interaction effects between exposure variables, we calculated smooth interaction functions of air pollution and temperature using R version 2.5.0 (R Foundation for Statistical Computing 2008).
To investigate the association between the alcohol intake variables we calculated Pearson's correlation coefficients.
To check for collinearity between the different predictor variables we calculated the variance inflation factors (VIF).
Instead, to assess the strength of the relationship between dependent and predictor variables, we calculated standardised effect sizes (partial r) from the t-values from multiple regressions [ 58].
To evaluate the group effect on the mean joint coupling variables we calculated the between-group coefficient of multiple correlation (CMCBG) between the mean waveforms of the obese and the control groups.
In order to test whether there is an association between two categorical variables, we calculate the number of individuals we would get in each cell of the contingency table if the proportions in each category of one variable remained the same regardless of the categories of the other variable.
To explore the crude relationship between each variable, we calculated the Spearman correlations between climate variables, air pollutants and childhood pneumonia (Table 1).
To identify genes with highly variable expression between individuals, we calculated the average expression and the SED of all probesets for all 6 conditions (3 types of cells and 2 stimulation timepoints).
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