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Typically, several components are needed to explain most of the variances in the data.
These factors may explain some of the variances in the data presented here and elsewhere [ 31, 37].
Linear discriminant analysis determines the discriminant function that maximises the variances in the data between groups while minimising the variances between members of the same group.
Although there might be trends that exenatide decreased the change in C-peptide and insulin levels and that exenatide increased the rise in leptin level 30 min after the orogastric glucose load, the variances in the data were too large to reach any statistical conclusions.
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Again, aiming duration accounted for the variance in the data.
Process indicators explained 22.5% to 30.6% of the variance in the data.
The models explain 41 65% (adj. D2) of the variance in the data.
A factor analysis reduced 33 soil physicochemical parameters to five factors that explained 72% of the variance in the data.
The model explained 69%% of the variance in the data.
EFA yielded a 2-factor solution accounting for 68% of the variance in the data.
The data show that three factors account for 93.8 % of the variance in the data.
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