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The analysis of the data followed the following steps: The first step in the analysis looked in to trend in each Health and Health related indicator.
In between, the data followed a steeply rising curve.
The data followed a trend among tech companies that showed an under-representation of women.
The release kinetics showed that the data followed Higuchi model and the main mechanism of drug release was diffusion.
[L] was the most dominant in jím, accounting for 70% of the data, followed by jiû and ji̍p.
It was found that the kinetic of adsorption was fast and the data followed the pseudo-second rate equation.
The D'Agostino and Pearson test [17] was applied to confirm that the data followed a Gaussian distribution.
Transcripts were initially read in full to gain an overall perspective of the data, followed by line by line open coding.
[L] was the most prominent realization, accounting for over 60% of the data, followed by [R], which accounted for over 10% of the data.
For mobile applications such as signature verification and handwritten analysis, PCA is applied initially to reduce the dimensionality of the data, followed by similarity measure.
However, the final step then uses those values to obtain predicted values of the data followed by conventional predictive mean matching.
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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