Univariate analysis is the simplest form of analyzing data. “Uni” means “one”, so in other words, your data has only one variable. It doesn't deal with causes or relationships (unlike regression) and its major purpose is to describe.
For example, the distribution of the educational background of students involves only one variable and can the analysis can be referred to as univariate analysis.
To know more: https://www.statisticshowto.datasciencecentral.com/univariate/
Multivariate analysis (MVA) involves observation and analysis of more than one statistical outcome variable at a time. The technique is used across multiple dimensions while taking into account the effects of all variables on the responses of interest, and the techniques are especially valuable when working with correlated variables. One example mentioned in class is Factor Analysis.
Specifically, if attempting to understand the difference between two variables at a time is called Bivariate analysis.
To know more: https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/multivariate-analysis/
https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/multivariate-analysis
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