---
categories: [Non-linear relationships]
---

# Modeling Non-linear Relationships

### Overview {.unnumbered .unlisted}

Textbook reading: Chapter 7: Moving Beyond Linearity.

All the pertinent material for this lesson is contained within the textbook readings.

After completing the reading for this lesson, please finish the Quiz and R Lab on Canvas (check the course schedule for due dates).

### Objectives {.unnumbered .unlisted}

Upon successful completion of this lesson, you should be able to:

------------------------------------------------------------------------

-   Extend simple linear regression to model the relationship between a response variable, Y, and a single predictor variable, X, in a flexible way using polynomial regression, step functions, regression splines, smoothing splines, and local regression.
-   Use generalized additive models (GAMs) to flexibly predict Y using several predictors, $X _ { 1 , } \dots , X _ { p }$.

------------------------------------------------------------------------

![](/assets/508l8thumb.png){#fig-nonlinear .lightbox fig-alt="false positive rate vs true positive rate line graph" fig-align="center" width="70%"}
