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Missing not at random (MNAR) (also known as nonignorable nonresponse) is data that is neither MAR nor MCAR (i.e. the value of the variable that's missing is related to the reason it's missing). [5] To extend the previous example, this would occur if men failed to fill in a depression survey because of their level of depression.
Most database and spreadsheet programs are able to read or save data in a delimited format. Due to their wide support, DSV files can be used in data exchange among many applications. A delimited text file is a text file used to store data, in which each line represents a single book, company, or other thing, and each line has fields separated ...
For instance, a SQL statement like SELECT * FROM city could be inserted as text within C source code, and during compiling it would be converted into a custom format that directly called a function within a library that would pass the statement into the SQL system.
One of the arguments against using an OODBMS is that it may not be able to execute ad-hoc, application-independent queries. [citation needed] For this reason, many programmers find themselves more at home with an object-SQL mapping system, even though most object-oriented databases are able to process SQL queries to a limited extent. Other ...
Comma-separated values (CSV) is a text file format that uses commas to separate values, and newlines to separate records. A CSV file stores tabular data (numbers and text) in plain text , where each line of the file typically represents one data record .
SQL was initially developed at IBM by Donald D. Chamberlin and Raymond F. Boyce after learning about the relational model from Edgar F. Codd [12] in the early 1970s. [13] This version, initially called SEQUEL (Structured English Query Language), was designed to manipulate and retrieve data stored in IBM's original quasirelational database management system, System R, which a group at IBM San ...
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There are several types of data cleaning, that are dependent upon the type of data in the set; this could be phone numbers, email addresses, employers, or other values. [ 26 ] [ 27 ] Quantitative data methods for outlier detection, can be used to get rid of data that appears to have a higher likelihood of being input incorrectly. [ 28 ]