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.
Topics covered in this course include:
- Linear regression: concepts, assumptions, application, and interpretations
- Diagnostics and validation of linear regression models
- Generalised linear models: beyond continuous outcomes
- Poisson regression: how to model counts and rates, and how this differs from linear regression
- Best practices in communicating results of regression analysis