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Willingness to pay (WTP) estimates were calculated by dividing attribute coefficients by the salary coefficient for both models.
We also calculated willingness to pay (WTP) estimates and confidence intervals by dividing attribute coefficients by the continuous salary coefficient.
Willingness to pay estimates were calculated by dividing attribute coefficients by the salary coefficient for each model.
Furthermore, by dividing the attribute evaluation weights, which are the parameters of the attributes under analysis, we can evaluate the weight of the cost burden and thus transform the unitless attribute evaluation into units of currency.
This is accomplished by dividing each numeric attribute A into m discrete intervals where D={[d0,d1],…d1, dm−1,(dm−1,d m ]} where d0 is the minimal value, d m is the maximal value and d i <di+1 for i = 0,1,..., m 1.
And we derived mortality rates either by dividing observed deaths attributed to pneumonia and influenza by average annual cases from a demographically-realistic SEIRS model or by multiplying those rates by ratios of (versus adding to them differences between) pandemic and pre-pandemic mortalities.
The relative importance of each attribute was calculated as the range of part-worths for the attribute divided by the sum of part-worth ranges for all attributes Open image in new window Fig. 3 Effects of attribute levels on health state preference: part-worth utilities (N = 30).
We then calculated the relative importance of each attribute as the range in utility estimates within an attribute divided by the sum of the ranges in utility estimates for all attributes, expressed as a percentage.
Dividing by, we get.
Dividing by two.
Within each general LC class in each zone, the percentage of tree canopy or impervious cover (p) was calculated as the number of sample points (x) hitting the cover attribute divided by the total number of interpretable sample points (n) within the general LC class (p = x/n).
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