PS04: Linear Regression — One Numerical Predictor
Overview
Practice simple linear regression with one numerical predictor — including visualisation, correlation, model fitting, interpretation, and prediction — using demographic and voting data from the 2016 US presidential election.
Read Chapter 5 of ModernDive before attempting this problem set.
Download
Download the problem set template, open it in RStudio, and complete the exercises directly in the document.
Setup
Run this at the top of your document to install and load the required packages:
if (!require(pacman)) install.packages("pacman")
pacman::p_load(dplyr, ggplot2, readr, moderndive)Exercises
Worked example: white poverty and Trump support
Follow a guided regression analysis — visualisation, correlation, model fitting, interpretation, and prediction.
Independent analysis: non-white population and Trump support
Apply the same workflow independently using a different explanatory variable.
Saving your plots
Remember you can save any plots you create to the figures/ folder using ggsave(). Use descriptive file names that reflect the content of the plot:
non_white_plot <- ggplot(data = trump, aes(x = non_white, y = trump_support)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
ggsave("figures/trump-support-vs-non-white.png", plot = non_white_plot,
width = 16/2, height = 9/2)When you are done, render to HTML and submit on Moodle. Name your file PS04_yourname.html.