bivariate analysis

This analysis, called a bivariate analysis [ 32 ] , represents an individual specimen as a vector in the x-y plane.

The main question therefore was: How does the univariate binary RSA pattern analysis (Method 2) correspond to the bivariate analysis of phase relations between the cardiac and the respiratory cycle (Method 1)?

The second approach uses a bivariate analysis that reveals intraspecimen variability as well as mean peak time to test differences in phase.

Univariate analysis contrasts with bivariate analysis - the analysis of two variables simultaneously - or multivariate analysis - the analysis of multiple variables simultaneously.

However, it is currently limited to the bivariate analysis and usually requires more samples for accurate estimation.

Variables identified as potential confounders by these bivariate analyses were included in multivariate models, as were the cofactors believed to be clinically relevant.

Subgroup comparison - the descriptive analysis of two variables - can be sometimes seen as a very simple form of bivariate analysis (or as univariate analysis extended to two variables).

Common forms of bivariate analysis involve creating a percentage table or a scatterplot graph and computing a simple correlation coefficient.

The main reason for differentiating univariate and bivariate analysis is that bivariate analysis is not only simple descriptive analysis, but also it describes the relationship between two different variables.