If we want to explore a relationship between two quantitative variables, we make a scatterplot of the data. The fun doesn't stop there. We describe pattern, talk about the type of correlation we see, and sometimes fit a line to the data so we can use the pattern to make predictions.
Constructing and interpreting scatterplots is the focus of this tutorial. You'll learn how to make a good scatterplot, and then how to describe what you see using positive and negative correlation and outliers.
Regression is fitting a line or curve to a pattern we see in a scatterplot. This tutorial focuses on linear regression, where you'll learn how to fit a line to data, describe features of that line, and use the line to make predictions.
Residuals measure how far away each data point is from a line that has been fit to the data in a scatterplot. A least-squares regression line tries to fit the data the best it can by making these residuals as small as possible, and we can measure how well a line fits using r-squared. This tutorial explores a few of the more advanced topics in linear regression.