When we are dealing with data analysis, we often come across terms such as univariate, bivariate, and multivariate. Let’s try and understand what these mean.
Univariate analysis: Univariate analysis involves analyzing data with only one variable or, in other words, a single column or a vector of the data. This analysis allows us to understand the data and extract patterns and trends from it. Example: Analyzing the weight of a group of people.
Bivariate analysis: Bivariate analysis involves analyzing the data with exactly two variables or, in other words, the data can be put into a two-column table. This kind of analysis allows us to figure out the relationship between the variables. Example: Analyzing the data that contains temperature and altitude.
Multivariate analysis: Multivariate analysis involves analyzing the data with more than two variables. The number of columns of the data can be anything more than two. This kind of analysis allows us to figure out the effects of all other variables (input variables) on a single variable (the output variable).
Example: Analyzing data about house prices, which contains information about the houses, such as locality, crime rate, area, the number of floors, etc.