# Univariate Dataset

A Univariate Dataset is a dataset that contains only one dataset variable.

**Context:**- It can range from being a Numerical Univariate Dataset to being a Categorical Univariate Dataset.
- It can be an input to a Univariate Data Analysis Task, such as a point estimation task.
- It can range from being a Real Univariate Dataset to being a Synthetic Univariate Dataset.

**Example(s):**- [math]\{1, 2.4, 0, -1, \infty\}[/math]
- a Univariate Timeseries.

**Counter-Example(s):**- a Multivariate Dataset, such as a bivariate dataset.
- a Categorical Dataset, such as
`{a, lask, 283}`

.

**See:**Forecasting Task, Univariate Expression, Univariate Function, Univariate Data, Bivariate, Trivariate, Quadrivariate, Multivariate, Univariate Timeseries.

## References

### 2016

- (Wikipedia, 2016) ⇒ https://en.wikipedia.org/wiki/univariate Retrieved:2016-5-9.
- … The term is commonly used in statistics to distinguish a distribution of one variable from a distribution of several variables, 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 term is applied with a whole time series as the object referred to: thus a univariate time series refers to the set of values over time of a single quantity. Correspondingly, a "multivariate time series" refers to the changing values over time of several quantities. Thus there is a minor conflict of terminology 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.