univariante

Univariante refers to analysis, modeling, or data description that involves a single variable. It is the simplest form of statistical analysis, focusing on the properties of that variable alone rather than on relationships with others.

In descriptive univariante analysis, the variable is summarized using numerical measures such as mean, median, and mode, as well as dispersion statistics like range, variance, and standard deviation. For categorical or qualitative data, frequency counts and the mode are common summaries. Distributional characteristics such as skewness and kurtosis may also be reported to describe shape.

Visual representations are often used to convey univariante information. Quantitative variables are typically shown with histograms or density plots, while qualitative variables are displayed with bar charts or pie charts. Box plots can illustrate central tendency, spread, and potential outliers in a concise way.

Univariante analysis also extends to time series, where a single variable is observed over time to examine trends, seasonality, and volatility. Examples include daily temperatures, rainfall totals, or stock closing prices. While univariante methods provide a concise description of one variable, they do not capture interactions or dependencies between multiple variables, which require multivariante or bivariate approaches.

In practice, univariante techniques are foundational for exploratory data analysis, data cleaning, and the initial assessment of data quality before moving to more complex analyses. See also multivariante analysis, descriptive statistics, and time series analysis.