Depicting the relationships between numerical variables with other

graphs

We started this chapter by developing summary statistics and making graphs of one numeric variable at

a time. One example was where we took seven measurements of diastolic blood pressure (DBP) from

a group of study participants and developed summary statistics. This is called a univariate analysis

because it only concerns one variable. But in the example of box plots in the preceding section, we

conducted a bivariate analysis because we were looking at the relationship between two variables in

a sample of patients from four different clinics. The two variables were enzyme levels, and source

clinic (Clinic A, B, C, or D). We could have done another bivariate analysis looking at two continuous

variables (such as two different enzyme levels in participants) using a scatter plot, which is covered

thoroughly in Chapter 16.

This chapter focused on univariate and bivariate summary statistics and graphs that can be developed

to help you and others better understand your data. But many research questions are actually answered

using multivariate analysis, which allows for the control of confounders. Being able to control for

confounders is one of the main reasons biostatisticians opt for regression analysis, which we describe

in Part 5 and Chapter 23. In these chapters, we cover the appropriate summary statistics and graphical

techniques for showing relationships between variables when setting up multivariate regression

models.