Univariate analysis is a fundamental technique used in statistical data analysis. A cause-and-effect connection is not present in this case because there is just one factor in the data. Imagine doing a survey in a classroom. The number of males and girls present would be counted by the analysts. The number is the sole variable and variable number in this set of data. A univariate analysis’ main objective is to describe the data in order to find patterns in it. To do this, the means, modes, medians, standard deviations, dispersion, etc. are evaluated.
Still can’t understand it. Why don’t you try a free video lecture. Understand it on Data Analyst Training.
Univariate analysis is the most fundamental kind of data analysis. Uni stands for one, hence there is only one variable in the data. Univariate analysis is primarily justified by the manner in which the data to describe. The analysis will make use of data, summarise the data, and search for trends.
A variable in univariate analysis is merely a circumstance or subset of the data. It might be regarded as a “category.” For instance, the analysis might take a factor like “age,” “height,” or “weight” into account. It doesn’t investigate multiple variables at once or consider how different variables relate to one another. The investigation of two or more variables & their relationship is referred to as bivariate analysis. The simultaneous study of three or even more variables is known as multivariate analysis.
WHAT PROCEDURE DOES UNIVARIATE ANALYSIS FOLLOW?
A univariate analysis can be carried out in a variety of ways, most of which are descriptive. These include histograms, bar charts, pie charts, frequency polygons, and frequency distribution tables.