Bivariate Relationships — Correlation and Scatterplots

In this mission, you’ll learn build on the modeling fundamentals you learned in the R modeling fundamentals lesson as we begin to take a closer look at linear models and the trend line.

As we work with these new models, we’ll also be reviewing some programming and statistics concepts from earlier in our Data Analyst in R path.

Most of this linear modeling lesson will focus on how we can assess bivariate relationships using scatterplots. We’ll look at factors like form, direction, the closeness of the fit for our model, and how to handle outliers.

We’ll also review some concepts related to correlation, including Pearson’s correlation coefficient and how it can be helpful in the context of linear modeling.

Objectives

  • Assess bivariate relationships using scatterplots
  • Learn how Pearson’s correlation coefficient can be helpful when modeling

Lesson Outline

  1. Introduction
  2. Data Suitable for Linear Regression
  3. Exploring Bivariate Relationships with Scatterplots
  4. Linearity
  5. Strength
  6. Outliers
  7. Outliers with Regression
  8. Correlation
  9. Recap
  10. Takeaways

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