Univariate (statistics)
term in statistics
Univariate is a term commonly used in statistics to describe a type of data which consists of observations on only a single characteristic or attribute. A simple example of univariate data would be the salaries of workers in industry. Similar to other data, univariate data can be visualized using graphs, images, or other analysis tools after the data is observed/collected, analyzed and reported with conclusions of the analysis.
Data types
Univariate data can either consist of non-numerical observations (such as eye colors like brown or blue) or numbers (such as height of 1.65 m, or mass of 70 kg). Generally, the terms categorical univariate data and numerical univariate data are used to distinguish between these types.
Categorical univariate data
Categorical univariate data consists of non-numerical observations that may be placed in categories. It includes labels or names used to identify an attribute of each element. Categorical univariate data usually use either nominal or ordinal scale of measurement.
Numerical univariate data
Numerical univariate data consists of observations that are numbers. They are obtained using either interval or ratio scale of measurement.
“Univariate (statistics)” enters the record as term in statistics. Crown Archives preserves that source wording while asking what Univariate, statistics and term can confirm, complicate or overturn.
Why this record matters
“Univariate (statistics)” is worth following because a concise public description often conceals a longer documentary argument. Here, Univariate, statistics and term provides the most credible route into that argument.
The citation trail is more important than the brevity of the summary: it shows where individual claims can be examined in context. The source revision retrieved here is dated Sep 18, 2026. The linked authority identifier is Q30678297. None of the 0 selected statements returned an explicit reference.
A concise general-reference account can conceal disagreements about scope, terminology or the weight assigned to individual sources. The source lead contains qualifying language; that uncertainty should survive quotation, summary and reuse. Authority statements aid reconciliation but still require their own references, qualifiers and ranks to be checked.
How to read it
Use the entry as an orientation point, then follow its citations and revision history. Names, dates and institutional relationships should be checked against the original record.
- Subject orientation
- Search vocabulary
- Locating named sources
The closest primary source, responsible institution and strongest cited specialist reference.
Three-step research path
- Establish the record: confirm the title “Univariate (statistics)”, its source revision and the description used here.
- Expand the search: follow Univariate (statistics) primary sources, Univariate (statistics) archive and Univariate research across catalogues and specialist indexes.
- Test the account: compare the strongest cited source with the responsible institution’s current record and note any disagreement.
Questions for further research
- Which source most directly establishes the central claim about “Univariate (statistics)”?
- Which cited source is closest to the event, object or claim?
- Which institution is responsible for the underlying evidence?
Search terms from this dossier
This entry incorporates text from “Univariate (statistics)” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.