Because different methods of obtaining H7N9 information varied significantly with some of demographic variables (indicated by the chi-square test on univariate analysis; Tables 3, 4), we conducted multivariate analyses to determine which independent variables are significant in logistic regression models, with non-significant variables also entered into the models.
The following deviation variables, indicated by the over bar, were defined: x ¯ = x − x s s, h, u x ¯ = u x − u x s s, h, y ¯ = y − y s s, h, u t o t ¯ = u t o t − u t o t, h.
For well-switching behavior and change in UAs/g Cr from baseline to follow-up, we additionally controlled for the number of low-As wells available within 200 m of the household, which was used as a continuous variable (indicated by model B).
Sleep problems, physical symptoms, emotional symptoms, and functional disability were the four latent variables considered in this study, with each latent variable indicated by two to six manifest variables.
Because this was a time of downsizing in Sweden, sickness presence could also be a potential confounding variable, indicated by a similar lack of effect of major downsizing on sickness absence rates.
From the study of SN Gorb's data, it is concluded that there are additional indicators for structural behaviour as well as the morphological variables indicated by Gorb, and these are flexible versus fixed bases to the shafts and degrees of resilience (stiffness, the inverse of Young's modulus) of the component material.
Note that TPG and Comorbidity are categorical variables indicated by asterisks.
The linearity of the relationship between each continuous independent variable and the logit transformation of the dependent variable was indicated by the Box Tidwell approach.
The relative strength of association of each variable is indicated by the standardised β coefficients, and is the variable of interest, although the unstandardardised regression coefficients are also intuitively meaningful.
Nevertheless, Equation 2 outperformed Equation 12 more often than not, and APAR predictions from Equation 12 were nearly always more variable, as indicated by the higher MSE in Table 5, Figure 7 and also Figure 3e compared with Figure 6b.
