This paper presents a new approach to salt sampling, using statistical and experimental design techniques to determine the degree to which analytical results are potentially affected by factors such as the sampling location and prevailing environmental conditions.
Correction for the small sample size using statistical methods (e.g., Student- t factor) should be considered if this approach is used to determine sampling and measurement precision.
We accounted for sampling weights and the clustered sampling design using statistical survey methods.
Next, all the risk factors retained from the screening were investigated for pair-wise collinearity for a sampled house using statistical methods appropriate for the types of variables.
RDS is a modified chain-referral sampling technique, using statistical weighting for network sizes of individuals to generate representative estimates.
This design allowed us to quantify the particular resources and conditions conducive to fire by contrasting pixels where fires occurred against a random sample of pixels using statistical models that estimate a relative probability of occurrence.
The treatments were performed in triplicate, and differences among samples were evaluated using statistical analyses.
The standard deviations on mean values of duplicate samples were analyzed using Statistical Package for Social Scientist (SPSS 2013), version 21.
The standard deviations on mean values of duplicate samples were analysed using Statistical Package for Social Scientist SPSSS, 2005), version 16.
Measures of neighborhood urban design, PA levels across multiple domains (leisure, transport, habitual, and overall), and body size will be used to model the associations and establish effect sizes in a diverse sample using apposite statistical modeling.
