How to describe bivariate data

Abstract

The role of scientific research is not limited to the description and analysis of single phenomena occurring independently one from each other (univariate analysis). Even though univariate analysis has a pivotal role in statistical analysis, and is useful to find errors inside datasets, to familiarize with and to aggregate data, to describe and to gather basic information on simple phenomena, it has a limited cognitive impact. Therefore, research also and mostly focuses on the relationship that single phenomena may have with each other. More specifically, bivariate analysis explores how the dependent ("outcome") variable depends or is explained by the independent ("explanatory") variable (asymmetrical analysis), or it explores the association between two variables without any cause and effect relationship (symmetrical analysis). In this paper we will introduce the concept of "causation", dependent ("outcome") and independent ("explanatory") variable. Also, some statistical techniques used for the analysis of the relationship between the two variables will be presented, based on the type of variable (categorical or continuous).

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