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A phenomenographic data analysis sorts qualitatively distinct perceptions which emerge from the data collected into specific "categories of description." [1] [2] [3] [8] The set of these categories is sometimes referred to as an "outcome space." These categories (and the underlying structure) become the phenomenographic essence of the ...
In statistics and econometrics, cross-sectional data is a type of data collected by observing many subjects (such as individuals, firms, countries, or regions) at a single point or period of time. Analysis of cross-sectional data usually consists of comparing the differences among selected subjects, typically with no regard to differences in time.
Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. [4] Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. [5]
Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. The data may also be collected from sensors in the environment, including traffic cameras, satellites, recording devices, etc.
Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes.
eXternal Data Representation (XDR) Sun Microsystems (creator) IETF (maintainer) — Yes STD 67/RFC 4506 Yes No Yes Yes Yes — XML: W3C: SGML: Yes W3C Recommendations: 1.0 (Fifth Edition) 1.1 (Second Edition) Partial (Efficient XML Interchange, Binary XML, Fast Infoset, XSD base64 data) Yes XPointer, XPath: XML schema, RELAX NG: DOM, SAX ...
Competency-based learning or competency-based education is a framework for teaching and assessment of learning. It is also described as a type of education based on predetermined "competencies," which focuses on outcomes and real-world performance. [1]
Orders of magnitude (data) Binary [bits] Decimal Item Factor Term Factor Term 2 −1: 10 −1: 0.415 bits (log 2 4/3) – amount of information needed to eliminate one option out of four. 0.6–1.3 bits – approximate information per letter of English text. [3] 2 0: bit: 10 0: bit 1 bit – 0 or 1, false or true, Low or High (a.k.a. unibit)