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Second order polynomial models (Equation 1) y = β 0 + ∑ j = 1 k β j x j + ∑ j = 1 k β jj x j 2 + ∑ i < j ∑ j = 2 k β ij x i x j (1)are most commonly used in RSM because they are flexible in describing data in which there is curvature.
This approach greatly enhances and facilitates the interpretation of observed dynamics by reducing the dimensionality of the problem, i.e. by describing data in time-independent spectra with their associated time constants.
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When pundits describe data in the 21st century, they often resort to the flood metaphor, describing how we are "inundated" or "awash" with data.
Usage of data coming from different data sources can be complicated if the sources describe data in incompatible ways.
A profile can be associated with multiple contexts or one context can describe data in multiple profiles.
In an attempt to represent the described data in a clear overview, we modeled the experimental data using the general framework described by Roehrl et al. [19].
Conventional statistical methods were used to summarize and describe data in tables and graphs.
Common Data Elements (CDEs) are well-defined XML metadata tags that can be used to consistenly describe data in different XML files.
To summarise, we may wish to describe data in its entirety, as the patient interval, or break it down further, into the appraisal interval and help-seeking interval.
Large patterns are more useful in describing data.
The other two vocabularies describe data entities in the domains of business and geographical location.
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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