At this point in our Linear Modeling in R course, you've already learned about how to fit and plot a linear model. Now, it's time to get into more depth and assess your model's accuracy so that you can effectively judge it (and potentially make improvements).

To assess your model effectively, you'll learn about calculating the Residual Standard Error and R Squared. You'll learn how to visualize the residuals, and more importantly, you'll learn why visualizing them can be important!

By the end of the mission, you will have built experience with the final, crucial step in any modeling task: assessing accuracy. 

Of course, as you work with the model, you'll also be reviewing programming and statistics concepts from earlier in our Data Analyst in R path.

Objectives

  • Calculate Residual Standard Error and R Squared
  • Learn why visualizing residuals is a good idea

Lesson Outline

1. Introduction
2. Plotting the Residuals
3. Hypothesis Testing
4. The t-statistic
5. The p-value
6. Confidence Intervals
7. Residual Standard Error
8. The R-squared statistic
9. R-squared and Correlation
10. Recap
11. Takeaways