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Data classification is the process of organizing data into categories based on attributes like file type, content, or metadata. The data is then assigned class labels that describe a set of attributes for the corresponding data sets. The goal is to provide meaningful class attributes to former less structured information.
The example above is the simplest kind of contingency table, a table in which each variable has only two levels; this is called a 2 × 2 contingency table. In principle, any number of rows and columns may be used. There may also be more than two variables, but higher order contingency tables are difficult to represent visually.
A pivot table is a table of values which are aggregations of groups of individual values from a more extensive table (such as from a database, spreadsheet, or business intelligence program) within one or more discrete categories. The aggregations or summaries of the groups of the individual terms might include sums, averages, counts, or other ...
Each entry in the table contains the frequency or count of the occurrences of values within a particular group or interval, and in this way, the table summarizes the distribution of values in the sample. This is an example of a univariate (=single variable) frequency table. The frequency of each response to a survey question is depicted.
Although widespread use of the term data processing dates only from the 1950s, [2] data processing functions have been performed manually for millennia. For example, bookkeeping involves functions such as posting transactions and producing reports like the balance sheet and the cash flow statement.
The distinction between quantitative and categorical variables is important because the two types require different methods of visualization. Two primary types of information displays are tables and graphs. A table contains quantitative data organized into rows and columns with categorical labels. It is primarily used to look up specific values.
This editing requires a certain understanding around the dataset and the ability to identify errors in data based on previous reports or information. This type of data editing is used to account for the differences between data fields or variables. See the example below. In the above table is an example of logical inconsistency in the data set.
Export (DBF): Specifies whether the product support exporting (saving) selected rows to a dBase Table file. Export (Excel): Specifies whether the product support exporting (saving) selected rows to an Excel file. Usually also implies capability to copy the rows to the clipboard (in some format) for pasting into Excel.