Overview

Overview

Recall from Lesson 3 that you learned how to create scatter plots with a best fit line. When you created this plot, you were visualizing the result of linear regression! In this lesson, we will delve deeper into linear regression, as well as return to the concept of correlation that we introduced earlier in the course. We will discuss the traditional way of conducting linear regression in addition to several built-in functions in Python. You will learn how to implement linear regression, run hypothesis tests for various parts of the linear regression analysis, and interpret results from the different analyses. 

Learning Outcomes

By the end of this lesson, you should be able to:

• assess whether two variables are associated based on their correlation coefficient

• explain the simple linear model; solve for the slope and intercept of a regression line in Python

• test a calculated slope value for significance

• interpret R-squared values

• contrast confidence intervals vs. prediction intervals for linear regression; produce both in Python

Lesson Roadmap

Questions?

If you prefer to use email:

If you have any questions, please send a message through Canvas. We will check daily to respond. If your question is one that is relevant to the entire class, we may respond to the entire class rather than individually.

If you prefer to use the discussion forums:

If you have questions, please feel free to post them to the General Questions and Discussion forum in Canvas. While you are there, feel free to post your own responses if you, too, are able to help a classmate.