univariaat

Univariaat, or univariate analysis, is a term used in statistics to describe the examination of a single variable at a time. It focuses on describing and summarizing the properties of that one variable without considering relationships to other variables. This contrasts with bivariate or multivariate analyses, which study two or more variables simultaneously.

In univariate analysis, the primary goals are to understand the distribution, central tendency, and variability of the variable. For numerical variables, common descriptive statistics include the mean, median, mode, variance, standard deviation, interquartile range, and various percentiles. Graphical tools such as histograms, box plots, and stem-and-leaf plots help visualize the distribution and identify features like skewness or outliers. For categorical variables, analysis centers on frequencies and proportions, often summarized with bar charts and mode.

Univariate distributions describe the possible values of a single variable and their probabilities. Examples include the normal, uniform, and exponential distributions, each characterized by parameters such as location and scale that define its shape and spread.

Applications of univariate analysis span data exploration, quality control, epidemiology, and early-stage statistical reporting. While it provides essential insights, it does not reveal associations or causal relationships between variables. To study such relationships, analysts progress to bivariate or multivariate methods that jointly analyze multiple variables.