Exploring bivariate numerical data

This course contains 6 segments:

Introduction to scatterplots

Let's go over the basics of how to construct a scatter plot and describe the relationship we see with its form, direction, and strength.

Correlation coefficients

We'll learn to calculate and use the correlation coefficient r to describe the strength and direction of a linear relationship.

Introduction to trend lines

When we see a pattern in a scatterplot, it's often useful to model that pattern with a trend line. This lesson covers how to informally fit a line to data, make predictions, and think about the slope and y-intercept of the trend line.

Least-squares regression equations

Once we see a linear pattern in a scatterplot, we fit a line to model the relationship and make predictions. But how do we know what line fits the data "best?" To solve this problem, we'll learn about residuals and least-squares regression .

Assessing the fit in least-squares regression

Let's learn how to measure how well a linear model actually fits the relationship in a scatterplot using residual plots and r-squared. We'll also look at how outliers can be influential points in least-squares regression.

More on regression

This is a more advanced collection of videos that goes into some more advanced topics and proofs in least-squares regression.