Chapter 7 Moving Beyond Linearity

Learning objectives:

Model relationships between a predictor and an outcome with

1. polynomial regression
2. step functions
3. regression splines
4. smoothing splines
5. local regression
6. generalized additive models

Chapter 7 resources:

1. The text at https://www.statlearning.com/
2. Cohort 1 videos
3. Author videos
4. Author slides
5. Emil Hvitfeldt’s Tidymodels examples
6. Kim Larsen’s GAM: The Predictive Modeling Silver Bullet
7. Noam Ross’ GAMs in R: A Free, Interactive Course using mgcv
8. onmee’s ISLR solutions for Chapter 7

Black motorcycle parked at road toward mountain

suppressPackageStartupMessages({
  library(splines)
  library(tidymodels)
  
  library(ISLR)
  library(boot)
  library(MASS)
  library(leaps)
  library(gam)
  library(ggplot2)
})

tidymodels_prefer()

#require(caTools)
attach(Wage)
attach(Auto)

## The following object is masked from Wage:
## 
##     year

## The following object is masked from package:lubridate:
## 
##     origin

## The following object is masked from package:ggplot2:
## 
##     mpg

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