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Here, we use a regression approach to derive, for all correlation strengths, the conditional level-crossing correlation functions in two continuous Gaussian processes.
Our first result provides an explicit solution to ν cond = 〈 s 1 ( t ) s 2 ( t + τ ) 〉 / ν 1 ν 2 that is valid for all correlation strengths and which comprises previously obtained limits, see discussion in Sect.
Table 2 reports the correlations between each of the concomitant variables and the difference in absolute calculation errors between the condition with articulatory suppression and the condition without, when interactivity level is low and high, and when collapsing over the two levels of interactivity (df = 50 for all correlation coefficients).
For all correlation matrixes obtained from different methods, we use the transformation: M i, j)→M i, j)/M'max to transfer the maximum value to 1. Obviously, we can see that the surface of our method has fewer peaks and more distinct maximum.
Spearman rank correlations were generated for all correlation tests.
Patients and controls were grouped together for all correlation analyses.
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The three velocity measures (VCL, VSL and VAP) were strongly positively correlated (r > 0.76, P < 0.001 for all correlations) and showed the same differences among treatment groups.
We show correlation coefficients and p values for those that showed statistical significant correlation Spearman's coefficients for all correlations Table 3 shows multiple linear regressions of those factors that significantly correlated with SGRQ Total scores divided by gender.
The bubble point pressure errors reported in this study, for all correlations, are among the highest reported in the literature.
More importantly, the table shows large range of error between minimum and maximum errors for all correlations.
Bonferroni-Holm- corrections for multiple comparisons adjusted for dependent measurements have been performed for all correlations (k = 10) [32].
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