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Patient outcome data is categorized in Table 2.
Given that education in our data is categorized in discrete cells (as discussed in the appendix), we continue to adopt a non-parametric approach.
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The acquired data were categorized in a Cartesian data sheet and graph.
Outcome data were categorized in terms of availability of mortality, survival, hospitalization, or complications data.
In addition, continuous data were categorized in a clinically meaningful way.
For comprehensive dissimilarity analyses, socio-economic data were categorized in two groups based on the median of their distribution.
If reasonable for calculation of the odds ratios, the data were categorized in up to four categories.
Data were categorized in to four groups (these groups were: socio-demographic characteristics, maternal health care use, child characteristics and mothers' knowledge of vaccine and vaccine preventable diseases) to see the association of explanatory variables with outcome variable.
Origins of interest in high-quality laboratory data is reviewed, in vitro data is categorized into property and simulation protocols, and two approaches are suggested for establishing clinical validity.
He emphasizes the need for businesses to own their data and information in a deep way i.e. have a solid understanding of where data is located, how data is categorized (documenting in a menu or guidebook helps), and refine/create a system for how data has and will be sorted in the future.
A three-dimensional approach to securing data in cloud is presented in [33], wherein data is categorized on the basis of a priority rating, which incorporates confidentiality, integrity, and availability.
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CEO of Professional Science Editing for Scientists @ prosciediting.com