9 Modeling Non-linear Relationships

Non-linear relationships

Overview

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

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, \(X _ { 1 , } \dots , X _ { p }\).

false positive rate vs true positive rate line graph

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categories: [Non-linear relationships]
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# 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:

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-   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, $X _ { 1 , } \dots , X _ { p }$.

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