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Values obtained at sea level and at high altitude were compared by a parametric Students paired t-tests.
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These regressors were treated as nuisance regressors; they were not modulated by a parametric regressor and were not analysed.
In this study, by a parametric representation of interval numbers, two parametric representations for interval-valued functions are presented.
Data were fitted by a parametric logistic equation.
The data violated assumptions of normality and homogeneity of variance (visual assessment of the frequency histogram and normal plot, Kolmogorov-Smirnov and Barlett tests), thus differences between treatments were assessed using a non-parametric Kruskal-Wallis (χ2) test (KW), followed by the non-parametric Student-Newman-Keuls (SNK) test to correct for multiple comparisons.
Non parametric student's t-test was used to analyse variables.
This estimation was performed by adjusting a parametric survival model to our data, using the survreg function [21] in R. The parametric model we used has the same fixed and random factors than the non-parametric model we described above.
A parametric test (t-student) was used for comparing two independent sample groups by means of the SPSS software (IBM Corporation, Armonk, NY, USA).
For continuous variables with a parametric distribution, the student's t-test was used; for nonparametric variables, the Kolmogorov-Smirnov (KS) test was used.
Where a common loudspeaker can be heard by anyone nearby, a parametric array can be heard only by people standing directly in the path of its sound beam.
Moreover, a key assumption is introduced by virtue of a parametric gap function.
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