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The reliability of kinetics parameters calculated from TPR data is tested by comparing calculated and experimental non-isothermal data.
Therefore, the consistency of the well data is tested by cross-plotting all well data on top of each other as shown in Figs. 8 and 9 where different color points in plot represent data from different wells.
Days are selected using a partitional clustering method known as the k-medoids method, and their ability to resemble the original data is tested by means of two quality indexes and a calendar visual inspection.
This is confirmed by Tables 7 and 8 below, where route choice behaviour in the warm-up phase is analysed and the difference between the warm-up and the non-warm-up data is tested by means of a non-parametric statistical test.
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The assumption of normality of the data was tested by Shapiro-Wilks tests.
The internal consistency of the survey data are tested by computing Cronbach's alpha.
The homogeneity of the station rainfall data was tested by the R-based RHtests_dlyPrcp software (Wang and Feng 2013).
The experimental data was tested by using artificial neural network (ANN) program and was in a good agreement with the estimated data.
The experimental data was tested by using artificial neural network (ANN) technique and was in a good agreement with estimated data.
The data was tested by varying the number of communities formed by using the infomap community-detection algorithm on the data; for each set of communities, we tested by varying the α value.
Normality of data was tested by use of the D'Agostino & Pearson omnibus normality test.
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