Regression with R

Course level: Foundation

Next live dates: TBC Contact us to request training details.

Course description

Regression analysis is one of the most widely used and powerful quantitative analysis methods used across almost every sector. Regression models were designed to investigate the relationship between an outcome of interest and a set of explanatory variables. These methods can then be used to make inferences about underlying relationships between variables, while accounting for confounding factors, and can be used to make predictions based on existing data.

This 2-day course provides a comprehensive understanding of regression analysis, including the theory behind these models, their application in R, validation techniques, and the interpretation of results. The course begins with an introduction to linear regression models, before extending these models to generalised linear models.

The course is designed to be highly interactive with a focus on practical applications, ensuring that you can immediately apply what you learn to your own data. Throughout the course, we will discuss best practices for reproducible coding.

Outline

Topics covered as part of this course include:

• Reminder of loading and tidying data in R using Tidyverse packages
• Correlation coefficients
• Linear regression: concepts, assumptions, application, and interpretations
• Diagnostics and validation of linear regression models
• Introduction to generalised linear models: beyond continuous outcomes
• Best practices in communicating results of regression analysis

Target audience

Anyone looking to gain a deeper understanding of their data, account for multiple variables within their analysis, and add one of the most powerful quantitative approaches to their toolkit.

This course does not require any prior statistical training, although understanding the interpretation of confidence intervals and p-values is helpful (for a brief introduction to inferential statistics, check out my free course here).

Participants are expected to be comfortable with loading and tidying data in RStudio using the Tidyverse package (I offer a short course on everything you need to know here!).

Learning outcomes

By the end of the course, participants can expect to have a robust understanding of regression analysis and its applications in R. They will be able to confidently perform and interpret both simple and multiple regression analyses, as well as generalised linear models. Additionally, they will learn how to diagnose and validate their models, ensuring the reliability and accuracy of their results.