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A linear regression model was fitted separately for EU and Non EU students, initially with all predictors (full model) then used variable selection to identify potentially useful predictors.
The stretcher securing the legs is clearly inspired in its shape by an old-fashioned hay rake, the model then used for clearing fields in Gloucestershire.
The model then used this observation to update its estimate of the variance using a fixed learning rate; the model is essentially a linear filter (see Appendix S1).
In each case, we estimated a phylogenetic tree for the dataset using the Jukes-Cantor model, then used this tree in estimating the rates.
The underlying regression model then used the four factor profiles and the corresponding voxel locations (obtained from the data of three participants other than the one that was being analyzed).
For each pseudo-absence run, in order to evaluate the predictive performance of a species distribution model, we used a random subset of 70% of the data to calibrate the model, then used the remaining 30% for evaluation, using a threshold independent method, the area under the relative operating characteristic curve (AUC) [35].
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The model then uses Equations (13), (15), and (18) for obtaining values for j1, j2, and n, respectively.
The model then uses emission optical properties to specify the scattering and step sizes of the fluorescent photons.
If you are making a 3D model, then use paper mache to make it.
This model is then used for model based control using internal model control (IMC).
The final multivariate model was then used to model the probability of transmission for combinations of individual patient characteristics.
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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