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The Morlet complex wavelet analysis was used to study the most important variability factors on time scales ranging from from 2 to 100 years, and their stability in time, which is shown in both time series studied.
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The main PCA modes define the most important variabilities, allowing us to link them to physical processes.
We also used a model selection process to identify the most important variables that explained the variability in the catch of bluefin and yellowfin tuna [18].
Gender of the household head is one of the most important variables that significantly affect the farmers' choice of climate variability adaptation options.
The three most important variables (Table 1) explained an average of 69% of data variability.
The one-hour repeatability is by far the most important random variability because most interventions we are interested in, such as bronchodilator response, can be measured within one hour.
However, the most important source of variability from the perspective of daily testing is assay process, or analytical variability.
PRIN1 and PRIN2 loadings plots (Figure 4) identify which wave numbers were the most important in the variability explained for each principal component [12],[12].
In order to decrease the dimensions of input dataset by summarizing their most important modes of variability and correlations to the reference dataset, a technique named singular value decomposition (SVD) is used.
In our study, year and climatic conditions were the most important source of variability.
Li et al. (2010) demonstrated grain weight as one of the most important characters enhancing variability.
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