What is Univariate Analysis?
Univariate analysis is the technique of comparing and analyzing the dependency of a single predictor and a response variable. The prefix "uni" means one, emphasizing the fact that the analysis only accounts for one variable's effect on a dependent variable. Univariate Analysis is thought to be one of the simplest forms of data analysis as it doesn't deal with causes or relationships, like a regression would. Primarily, Univariate Analysis simply takes data and provides a summary and associated patterns.
How does Univariate Analysis work?
Univariate Analysis works by examining the effects of a singular variable on a set of data. For example, a frequency distribution table is a form of univariate analysis as frequency is the only variable being measured. Alternative variables may be age, height, weight, etc., however it is important to note that as soon as a secondary variable is introduced it becomes bivariate analysis. With three or more variables, it becomes multivariate analysis.
Univariate Analysis is a common method for understanding data. Another common example of univariate analysis is the mean of a population distribution. Tables, charts, polygons, and histograms are all popular methods for displaying univariate analysis of a specific variable (e.g. mean, median, mode, standard variation, range, etc).