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Aggregate data is high-level data which is acquired by combining individual-level data. For instance, the output of an industry is an aggregate of the firms’ individual outputs within that industry. [1] Aggregate data are applied in statistics, data warehouses, and in economics. There is a distinction between aggregate data and individual data.
The United States Geological Survey explains that, “when data are well documented, you know how and where to look for information and the results you return will be what you expect.” [2] The source information for data aggregation may originate from public records and criminal databases.
Disaggregated storage is a form of scale-out storage, built with some number of storage devices that function as a logical pool of storage that can be allocated to any server on the network over a very high performance network fabric. Disaggregated storage solves the limitations of storage area networks or direct-attached storage.
Names and fine-level geographical data are removed, some data items are altered as necessary to make it impossible to identify individuals, and small ethnic categories are merged. [2] The International Household Survey Network has developed tools and guidelines to help interested statistical agencies improve their microdata management practices.
A descriptive statistic is used to summarize the sample data. A test statistic is used in statistical hypothesis testing. A single statistic can be used for multiple purposes – for example, the sample mean can be used to estimate the population mean, to describe a sample data set, or to test a hypothesis.
Data analysis; Data classification (business intelligence) Data entry; Data entry clerk; Data onboarding; Data processing (disambiguation) Data processing inequality; Data processing machine; Data processing unit; Data reporting; Data scraping; Data sonification; Data validation; Disaggregated storage; DOME MicroDataCenter; DOME project
The concept of data type is similar to the concept of level of measurement, but more specific. For example, count data requires a different distribution (e.g. a Poisson distribution or binomial distribution) than non-negative real-valued data require, but both fall under the same level of measurement (a ratio scale).
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]