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Signals corresponding to the solvent, TMS, and noise from water suppression were removed from the data set prior to statistical analysis.
The water quality parameters, their units and the basic statistics of the data set on river water quality for the four seasons are summarized in Table 1.
Multivariate statistical techniques, such as cluster analysis (CA), factor analysis (FA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the data set on water quality of the Gomti river (India), generated during three years (1999 2001) monitoring at eight different sites for 34 parameters (9792 observations).
The data set used in this study comprises wave wind data gathered from deep water locations in Lake Michigan.
The data set used in this study comprises of fetch-limited wave data and over water wind data gathered from deep-water location in Lake Ontario.
The data set used in this study comprises wave and wind data gathered from deep water location in Lake Ontario.
Defining the data set.
Moyel (2014) used principal component analysis (PCA) and cluster analysis (CA) for the assessment of the water quality data set.
Cyanobacteria-like genes were most frequent among amplicons from the warmest waters, but overall the data set was dominated by nifH sequences most closely related to non-cyanobacteria.
PC analysis identified five principal components accounting for 95.7% of the total variation in the original water quality data set during dry season.
We found decreasing concentration trends in the weekly water quality data set in this catchment, with water quality at most stations affected by the PWS schemes.
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