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When comparisons are possible, will utilize t tests for independent samples for continuous data, and χ tests for frequencies.
Analysis were done with t-test for independent samples for continuous variables and chi-squared test for categorical variables.
Differences between two independent samples for continuous data were analyzed using the Mann-Whitney U test (since the distributions of variables were different from normal).
Clinical characteristics of the sample stratified by age were examined with chi-square tests for categorical variables and t-tests for independent samples for continuous variables.
These comparisons were made using a chi-squared test for categorical variables and Student's t-test for independent samples for continuous variables.
We first studied whether there were any significant baseline group differences using t test for independent samples for continuous variables and χ tests for categorical variables.
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Study sample for continuous trait analysis For this part of the study we used participants from the ongoing New Hoorn Study, a population-based cohort study in the Netherlands, which examines potential determinants of glucose intolerance and related disorders [ 14, 15].
We used one-way analyses of variance (for large sample size) and Mann–Whitney U-tests (for small sample size) for continuous variables, and chi-square tests for comparing categorical variables between two groups.
We then queried the samples for 19 continuous bioclimatic variables (1-km2 spatial resolution [ 61]) and 55 binary vegetation variables (0.01-km2 0.01-km2respatialn, Califoresolutionife Habitat Relationships System).
Also, [14] [17] and [18] [21] are for example works in the direction of sampling analysis for continuous and discontinuous eigenproblems, respectively.
Missing data were imputed with the sample median for continuous or ordinal variables, and the mode for dichotomous variables.
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