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They prove that the first and second models are equivalent to the deterministic expected value one.
In the first and second models, the dependent variables were MS/P (VAS scores) in the five areas at baseline and follow-up.
In the first and second models, the dependent variables were VAS scores (pain intensities of each pain) at baseline and post-treatment, respectively.
Interestingly, our average AUC values are much better, with values of 0.76 and 0.85 for the first and second models, respectively.
The Pearson correlation coefficients for the estimated values were 0.89 and 0.90 on average for the first and second models, respectively.
The expected value of the log likelihood-ratio for the i-th cell,, is expressed by (18 where superscripts 1 and 2 refer to the first and second models, respectively.
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Specificity obtains highest values for the first and second model.
However, some coefficients seem to be counterintuitive as travel time to the nearest airport in the first analysis and unemployment in the first and second model are positive.
SVM needed only 4 variables to make its prediction in both models, whereas MLR needed 7 and 8 variables in the first and second model respectively.
Analytical expressions for the radiated sound pressure are obtained for the first and third models.
Figure 8 shows the forecast accuracy of the following models: (i) PC, (ii) PC using only factors that contribute to reducing the forecast error denoted as PC (2), (iii) PLS, (iv) PLS (2), (v) the average between the first and third models, and (vi) the average between the second and fourth models.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com