In the previous chapter, we looked at several different ways to graph one-variable data. By choosing from
dotplots, stemplots, histograms, or boxplots, we were able to examine visually patterns in the data. In this
chapter, we consider techniques of data analysis for two-variable (bivariate) data. Specifically, our
interest is whether or not two variables have a linear relationship and how changes in one variable can
predict changes in the other variable.
example: For an AP Statistics class project, a statistics teacher had her students keep diaries of
how many hours they studied before their midterm exam. The following are the data for 15 of
the students.The teacher wanted to know if additional studying is associated with higher grades and drew the
following graph, called a scatterplot. It seemed pretty obvious to the teacher that students who studied
more tended to have higher grades on the exam and, in fact, the pattern appears to be roughly linear.