What do you mean by Univariate, Bivariate and Multivariate?
Univariate data consists of only one variable. The analysis of univariate data is the simplest form of analysis since the information deals with only one quantity that varies. It does not deal with causes or relationships. The main purpose of this analysis is to describe the data and find patterns that exist within it. The pattern description found in this type of data can be made by drawing conclusions using central tendency measures mean, median and mode, dispersion or spread of data that is range, minimum, maximum, quartiles, variance and standard deviation and by using frequency distribution tables, histograms, pie charts, frequency polygon and bar charts.
Bivariate data consists of two different variables. The analysis of Bivariate data deals with causes and relationships. The analysis is done to find out the relationship between the two variables. Thus, bivariate data analysis involves comparisons, relationships, causes and explanations. These variables are frequently plotted on X and Y axis on the graph for better understanding of data and one of these variables is independent while the other is dependent.
Multivariate data consists of three or more variables. It is comparable to bivariate but contains more than one dependent variable. The ways to perform analysis on this data depends on the goals to be achieved. Some of the techniques are regression analysis, path analysis, factor analysis and multivariate analysis of variance (MANOVA).