An Introduction to Linear Modelling in R

Artwork by @AllisonHorst

You

• Are familiar with R.

• Are new to linear modelling or haven't covered it in a while.

• Are new to linear modelling in R.

Packages and requirements

• For this adventure you'll need the tidyverse meta-package, broom to tidy our models, and GGally to plot our coefficients.

Learning Objectives

In this course we explore linear models and their capabilities using a simulated dataset on salaries from different University departments.

Exploratory Data Analysis

• What is the difference between a continuous and categorical variable?

• What is variation and covariation?

• Where does Exploratory Data Analysis fit in with analysis?

• How to use plots to explore variation in

  • A continuous variable
  • A categorical variable

• How to use plots to explore covariation between

  • Two continuous variables
  • A categorical and continuous variable.

Model Basics and Construction

• What is a model family and fitted model?

• What is the difference between a response and an explanatory variable?

• How to construct a linear model in R.

• What are the slope and intercept in a linear model?

• Picking out key information from the model table

• How to extract specific parameters from the model object

Assessing Model Fit

• How to pick out key information from the table from a fitted model.

• How to inspect model residuals to assess model fit.

• How to use Adjusted R-squared and AIC to compare models.

Slides and Exercises

Artwork by @AllisonHorst

Course Instructions

Follow the slides and complete the exercises.

To view the presenter notes in the slides, type 'p'. If you would like to edit or adapt the slides you will need to install the package Xaringan and follow the instructions in the link.