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In mathematics, a **univariate** object is an expression, equation, function or polynomial involving only one variable. Objects involving more than one variable are *multivariate*. In some cases the distinction between the univariate and multivariate cases is fundamental; for example, the fundamental theorem of algebra and Euclid's algorithm for polynomials are fundamental properties of univariate polynomials that cannot be generalized to multivariate polynomials.

In statistics, a univariate distribution characterizes one variable, although it can be applied in other ways as well. For example, univariate data are composed of a single scalar component. In time series analysis, the whole time series is the "variable": a univariate time series is the series of values over time of a single quantity. Correspondingly, a "multivariate time series" characterizes the changing values over time of several quantities. In some cases, the terminology is ambiguous, since the values within a univariate time series may be treated using certain types of multivariate statistical analyses and may be represented using multivariate distributions.

In addition to the question of scaling, a criterion (variable) in univariate statistics can be described by two important measures (also key figures or parameters): Location & Variation.^{[1]}

- Measures of Location Scales (e.g. mode, median, arithmetic mean) describe in which area the data is arranged centrally.
- Measures of Variation (e.g. span, interquartile distance, standard deviation) describe how similar or different the data are scattered.