Exploring ANOVAs in R

R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.

This half-day course covers one and two-way Analyses of Variance (ANOVA) and their non-parametric counterparts in R. To better understand the tests, assumptions and associated concepts, we will be using a dataset containing the Mathematics scores of secondary students. This dataset also includes information regarding their mother’s and father’s jobs and education levels, the number of hours dedicated to study, and time spent commuting to and from school. Lifestyle information about alcohol consumption habits, whether the students have quality relationships with their families and whether they have free time after school is included in this dataset.

You'll learn:

• Basic statistical theory behind ANOVAs
• How to check that the data meets the assumptions
• One-way ANOVA in R and post-hoc analysis
• Two-way ANOVA plus interaction effects and post-hoc analysis
• Non-parametric alternatives to one and two-way ANOVA

Prerequisites:

This course assumes an intermediate level of programming proficiency, plus familiarity with the syntax and functions of the dplyr and ggplot2 packages. Experience navigating the RStudio integrated development environment (IDE) is also required.

If you’re new to programming in R, we strongly recommend you register for the Learn to Program: R, Data Manipulation and Visualisation in R workshops first.