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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.
Aggregate income [1] [2] [3] is the total of all incomes in an economy without adjustments for inflation, taxation, or types of double counting. [4] Aggregate income is a form of GDP that is equal to Consumption expenditure plus net profits. 'Aggregate income' in economics is a broad conceptual term.
Add a calculator widget to the page. Like a spreadsheet you can refer to other widgets in the same page. Template parameters [Edit template data] Parameter Description Type Status id id The id for this input. This is used to reference it in formula of other calculator templates String required type type What type of input box Suggested values plain number text radio checkbox passthru hidden ...
A social accounting matrix (SAM) represents flows of all economic transactions that take place within an economy (regional or national).It is at the core, a matrix representation of the national accounts for a given country, but can be extended to include non-national accounting flows, and created for whole regions or area.
In order to calculate the average and standard deviation from aggregate data, it is necessary to have available for each group: the total of values (Σx i = SUM(x)), the number of values (N=COUNT(x)) and the total of squares of the values (Σx i 2 =SUM(x 2)) of each groups. [8]
Templates used in the creation and formatting of tables and columns. See also {{ List to table }} and its related Category:Articles requiring tables ; and Category:Multi-column templates for simple columns without tables.
This is a documentation subpage for Template:Table Age Calculator. It may contain usage information, categories and other content that is not part of the original template page. Usage
This complexity should be transparent to the users of the data warehouse, thus when a request is made, the data warehouse should return data from the table with the correct grain. So when requests to the data warehouse are made, aggregate navigator functionality should be implemented, to help determine the correct table with the correct grain.