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We therefore modeled the data using the Binomial distribution.
First, we modeled the data with a mirror symmetric pair of dipoles.
This is not surprising because we modeled the data on a set by set basis, which tends to increase the variability.
We modeled the data by a minimizing a weighted χ2 using the program gnuplot [ 56].
Consequently, we modeled the data using predictive methods instead of explanatory methods[ 12].
The two evolutionary models that better modeled the data were determined according to the Akaike Information Criterion AICC) [ 38].
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PT processed and modelled the data.
b) Data modelling: the data was represented in a diagram (see Figure 1).
And the results depend on the assumptions used to model the data".
Hence, the chosen model appears to be suitable to model the data.
Standard models such as spherical, exponential, Gaussian, and power were used to model the data set.
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