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Plotting methods involve visualizing the Kaplan-Meier survival curves for the variable tested for non-proportionality.
Age was an independent variable tested for the influence on the generation of ablations and pallial growth.
That is, always begin with simple, one variable at a time analyses, such as multiple single-variable tests for association or significance.
bP values in ANOVA (for age) or Chi-square (for other variables) tests for differences among the three groups.
Operational variables tested for the aroma separation included feed flow rate and aroma feed concentration.
The variables tested for significance in the model were chosen for their potential to be related to mortality.
Most of the variables tested (for example: age, residence, ethnicity etc).
All variables tested for inclusion in the final model were noted.
Table 2 contains a summary of variables tested for their relationship with key player status.
For the major variables tested for risk, we had sufficient sample numbers to achieve 80% power at 5% significance level.
Variables tested for their association with a higher stigma score (defined as ≥3 negative responses) are summarized in Table 3.
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