Chapter 1: Warm-up Exercises

This appendix collects exercises for each chapter of the book.

Chapter 2: Introduction

Chapter 3: Getting Started

Chapter 4: Plots of Multivariate Data

Exercise 1 Using the Salaries dataset, create one or more plots to compare different smoothing methods for the relationship between yrs.since.phd and salary shown in Figure 4.5. Include linear regression, quadratic polynomial, and loess smoothers,

library(ggplot2)
data(Salaries, package = "carData")
# Your code here

Exercise 2 One alternative to a loess smooth, which allows a span argument to control the degree of smoothing is a natural spline, that can be used in geom_smooth() using the argument formula = y ~ splines::ns(x, df=), where df is the equivalent number of degrees of freedom for the spline smoother. Re-do Exercise 1, but trying out this smoothing method for several values of df.

Chapter 5: Dimension Reduction

Chapter 6: Overview of Linear models

Chapter 7: Plots for Univariate Response Models

Chapter 8: Topics in Linear Models

Chapter 9: Collinearity & Ridge Regression

Chapter 10: Hotelling’s \(T^2\)

Chapter 11: Multivariate Linear Models

Chapter 12: Visualizing Multivariate Models

Chapter 13: Visualizing Equality of Covariance Matrices

Chapter 14: Multivariate Influence and Robust Estimation

Chapter 15 (Appendix): Discriminant Analysis