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Appropriate landform parameters have been derived, indicating temperature and moisture distribution, exposure towards wind, snow etc.
The analysis was carried out on a sample of 1700 architectural units grouped into 22 types taking into account size, spatial distribution, exposure, building features.
Because of their skewed distribution, exposure and absenteeism were presented as median and interquartile range (IQR).
The data were too sparse to stratify the correlations according to the water distribution, exposure estimation, and sampling characteristics.
These include shared risk factors such as gender distribution, exposure to immunosuppressive drugs and possible shared genetic backgrounds of both disorders.
In the prior validation of the manual method, we compared measured and modeled PCE concentrations in the water samples according to water distribution, exposure estimation, and sampling characteristics.
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Because 1-OHP exhibited non-normal distributions, exposure data were log-transformed to better approximate the assumed normality of the statistical tests.
Due to the skewed exposure distributions, exposure medians and interquartile ranges (Q25 – Q75) were calculated for total PM2.5 as well as the following metals: aluminum, chromium, copper, iron, lead, manganese, nickel, and zinc.
Current approaches also ignored an expected log-normal distribution of exposure data within an exposure category.
Table 2 shows the distribution of exposure for self-reported and directly measured SHS exposure.
For each risk factor examined in the GBD 2010 project (Lim et al. 2012), the distribution of exposure was compared with an alternative (counterfactual) distribution termed the theoretical-minimum-risk exposure distribution (TMRED).
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