Chapter 1: Introduction

  • This chapter covers the basics of why you might want to use R, the different aspects of the RStudio interface, and some basic exercises in math that expose you to numbers, operators, functions, and the like.

  • Chapter 1 Supplemental materials

Chapter 2: Handling Data, sorting, and aggregation

Chapter 6: Colors and Advanced Graphics functions.

Chapter 7: Random Variables, Probability, Parameter estimation

  • This chapter provides a brief refresher on probability theory, random variables, and inferential statistics. It includes examples of estimating parameters using R functions.

  • Chapter 7 supplemental materials
This chapter is split into two videos/class days:

Chapter 8: Statistical Testing

Chapter 9: Introduction to Linear Regression

  • This chapter covers basics of linear regression in R, with a focus on estimating parameters of a linear model using eyeball, least-squares, and quantile regression approaches.

  • Chapter 9 supplemental materials
Link here

Chapter 10: Testing the Linear Model

  • This chapter covers methods for testing linear regression models, focusing on the logic of doing t-tests to examine linear coefficients. Also, examines Bayes factor regression, using categorical predictors, and factor:continuous predictor interactions.

  • Chapter 10 supplemental materials
Link here

Chapter 11: Comparing Regression Models, Variable Selection, and Prediction

Chapter 12: Orthogonality, Identifiability, and Limitations of Regression Models


Chapter 18: Factorial ANOVA: Main effects and interactions; effect sizes in ANOVA

Effect sizes in ANOVA:

Chapter 19 :Analysis of Covariance (ANCOVA)

Chapter 19: Advanced ANOVA: Within-subject designs, random versus fixed factors, and repeated measures