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This is encouraging, as it indicates that the fundamental principles of these distributions can be fixed, and that the datasets can be described by parametric statistical models.
When plotting SC versus SN at the given latencies corresponding to each peak crosscorrelation, the association between the long term skin cancer and sunspot number datasets can be described by a linear relationship with a linear coefficient.
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According to Asgharzadeh et al. (2007), each component of the GGT can form its own relationship between the model and dataset and can be described as: T_{ij} = G_{ij} mquad i,j = x,y,z (3).
This confirms that the best representation of the dataset should be in three dimensions, which can be described by axes entitled F1, F2 and F3.
The ICC for a particular outcome can be described as the amount of variation in the dataset that can be explained by the variation between clusters.
Volcanic vulnerability and fragility functions can be described by linear equations applied to either the whole dataset or to individual segments; this is our preferred method for limited datasets.
The latter can be described as the following three simple steps: 1. Read data from a web address, representing an ot Compound or an ot:Dataset instance; 2.
Really big datasets can be a mess.
More detail on these datasets can be found in reference1.
This essentially means making data readable by other machines, so that datasets can be aggregated and compared.
Now, datasets can be linked through behavioral fingerprints.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com