Univariate, Bivariate and Multivariate data – Amplified with an example

Example: If we are interested in studying the efficacy of medicine B on 20 individuals, the terms univariate, bivariate and multivariate can be explained in the following manner. The univariate, bivariate and multivariate data collected will be used for wider statistical inference (sample to population).

Univariate data: If we record data on say Blood Pressure of these 20 individuals, where readings on blood pressure is an indicator of impact of medicine B on the health of these individuals, then this data is a univariate data.

Bivariate data: If we record data on say Blood Pressure and Cholesterol levels of these 20 individuals, where Blood Pressure and Cholesterol are indicators of impact of medicine B on the health of these individuals, then this is a bivariate data. The interdependence between change in Blood Pressure levels and Cholesterol levels as an impact of medicine B can also be further analyzed.

Multivariate data: If we record data on say blood pressure, cholesterol, weight and blood sugar levels of an individual here then it is called a multivariate data. In this case interrelationship between these variables can be minutely analyzed. The dynamics of change in the value of one variable as a result of change in other variable can be statistically predicted. The collective effect of all the other variables (say cholesterol, weight and blood sugar) on a single variable (say blood pressure) can also be studied and predicted.