R Tutorial: Visualizing bivariate relationships

Want to learn more? Take the full course at https://learn.datacamp.com/courses/correlation-and-regression-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi, I’m Ben Baumer. I’m an Assistant Professor in the Statistical & Data Sciences Program at Smith College and I’ll be your instructor for this course on correlation and regression. In the previous courses, you've learned how to describe the distribution of a single variable. This is useful, but in many cases, what we are more interested in is understanding the relationship between two variables. In particular, in this course you will learn techniques for characterizing and quantifying the relationship between two numeric variables. In a statistical model, we generally have one variable that is the output and one or more variables that are the inputs. We will refer to the output variable as the response and we will denote it with the letter y. In other disciplines or contexts, you may hear this quantity called the dependent variable. More generally, the response variable is a quantity that we think might be related to the input or explanatory variable in some way. We typically denote any explanatory variables with the letter x. In this course, we will have a single explanatory variable, but in the next course, we will have several. In other fields, these can be called "independent" or "predictor" variables. Just as you learned to visualize the distribution of one variable with a histogram or density plot, statisticians have developed a commonly used framework for visualizing the relationship between two numeric variables: the scatter plot. The scatter plot has been called the most "generally useful invention in the history of statistical graphics”. It is a simple two-dimensional plot in which the two coordinates of each dot represent the value of one variable measured on a single observation. By convention, we always put the response vari