This workshop is designed to increase participants’ understanding of statistical relationships between data. It introduces principles and methods of regression models using R, and how to interpret relationships between variables. The course covers basic principles of regression methods through to interpreting the output of statistical analyses, and also includes practical sessions giving hands-on experience with regression analysis in R.
The workshop is recommended for researchers who wish to expand their skills into regression methods and who are considering using regression approaches in their research. Participants are expected to have a basic familiarity with the concepts of descriptive statistics and elementary statistical hypothesis testing.
The workshop is applicable for all disciplines, although examples and exercises will be based around clinical and biological datasets.
Learning Objectives
During the workshop, participants will learn how to:
- Understand the principles of linear regression methods
- Identify the appropriate correlation or regression analysis for a dataset
- Carry out regression analysis using R
- Interpret and report on the results of that analysis
Syllabus
Topics covered during the workshop will include:
- An introduction to continuous, discontinuous and categorical variables
- Understanding the relationship between variables and plotting that relationship graphically
- Calculating parametric and non-parametric correlation
- Performing simple and multiple linear regression
- Assumptions, errors, and what can go wrong in regression analysis
Prerequisites
Prior knowledge of R is required (the R for Reproducible Scientific Research Software Carpentry workshop is highly recommended) as the basics of R will not be covered.