Editing Data set (section)

==Properties==
Several characteristics define a data set's structure and properties.  These include the number and types of the attributes or variables, and various [[statistical measure]]s applicable to them, such as [[standard deviation]] and [[kurtosis]].<ref>{{Cite book |url=https://books.google.com/books?id=uTzeRZFmaBgC&pg=PA100 |title=Principles of data mining and knowledge discovery |author=Jan M. Żytkow, Jan Rauch |isbn=978-3-540-66490-1 |year=2000|publisher=Springer }}</ref>

The values may be numbers, such as [[real number]]s or [[integer]]s, for example representing a person's height in centimeters, but may also be [[nominal data]] (i.e., not consisting of [[Number|numerical]] values), for example representing a person's ethnicity. More generally, values may be of any of the kinds described as a [[level of measurement]]. For each variable, the values are normally all of the same kind. [[Missing values]] may exist, which must be indicated somehow.

In [[statistics]], data sets usually come from actual observations obtained by [[sampling (statistics)|sampling]] a [[statistical population]], and each row corresponds to the observations on one element of that population. Data sets may further be generated by [[algorithms]] for the purpose of testing certain kinds of [[software]].  Some modern statistical analysis software such as [[SPSS]] still present their data in the classical data set fashion.  If data is missing or suspicious an [[imputation (statistics)|imputation]] method may be used to complete a data set.<ref name="sde">{{cite book |title=Statistical Data Editing: Impact on Data Quality: Volume 3 of Statistical Data Editing, Conference of European Statisticians Statistical standards and studies |author=United Nations Statistical Commission |author2=United Nations Economic Commission for Europe |year=2007 |publisher=United Nations Publications |isbn=978-9211169522 |page=20 |url=https://unece.org/DAM/stats/publications/editing/SDE3.pdf }}</ref>