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Pooled results were constructed by using either the fixed-effect model when significant heterogeneity was absent or the random-effect model when significant heterogeneity was present [17].
For each series of experiments the effects of bacterial density, temperature and humidity of the flight chamber, air pressure, flight chamber (Figure 1: A, B, C), day and time of testing on the relative attractiveness were tested and fitted as parameters in the GLM model when significant.
Pooled results were constructed by using either the fixed-effect model when significant heterogeneity was absent or the random-effect model when significant heterogeneity was present [ 17].
Variables were kept in the model when significant at P < 0.05.
Then curve fit estimation was carried out to validate the regression model when significant correlation was fulfilled.
All variables were entered simultaneously into the model, and were kept into the model when significant at 5%.
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Quadratic and random slope terms were included in the models when significant.
Interactions of age, sex, and calendar period with education, cohabitation status, income, and attachment to work market were tested and are reported in the confounder-adjusted models when significant (Table 2).
Subsequently, independent variables of interest (p-value < 0.10) were included in the multiple regression analysis for each physical outcome measure and variables were retained in the final multivariate linear regression model when statistically significant (p < 0.05) or statistically significant in a multivariate model on another outcome measure.
The constant (intercept) was only included in the model when statistically significant [ 21].
The interactions between all these covariates were tested and included in the model when statistically significant.
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