Short Course: Fundamentals of Regression in R

Course Overview

This course provides a comprehensive hands-on introduction to regression analysis techniques The course content is designed for researchers with some prior knowledge of basic statistical testing, such as t-tests, p-values, confidence intervals and simple linear regression. The primary focus is on developing a conceptual understanding of regression models through numerous examples. There will be a strong emphasis on practical implementation in R, and interpretation of output. Approximately half the time will be dedicated to practical hands-on sessions.

The core content starts from linear models with more than one variable, enabling research questions like "What is the effect of this treatment/intervention after adjusting for confounding variables?" or "What is the relationship between two variables while controlling for other factors?" We then cover interactions between variables in linear models, enabling research questions like: "How does the effect of the treatment depend on some other variable? Is the treatment effect different between groups?" and "How is the relationship between two variables modified by some other variable?"

Fundamental regression concepts and skills that arise in regression, like multicollinearity, multiple testing, model selection, generalizing the linear model to data that is non-normal (e.g., binary response and count data), and regression with non-linear relationships between variables, are all covered in this course. By the end of this course, you will have a foundation in regression modelling techniques with the practical experience in R needed for more advanced regression methods like mixed models, longitudinal data analysis, survival analysis, meta-analysis, multivariate analysis, ordinal and multinomial regression, spatial regression and other extensions.

Course outline

Day 1: Revision, Multiple Regression Introduction and Extensions

Day 2: Morning/Afternoon: Multiple Comparisons/Model Selection

Day 3: Morning/Afternoon: Generalized Linear Models (GLMs)/Generalized Additive Models (GAMs)

Accessibility

This is an in person course. If you wish to participate, but in person attendance is not accessible to you (e.g. you have hearing or vision impairment, parenting responsibilities, live too far) please email Eve (eve.slavich@unsw.edu.au) to arrange online attendance. The virtual component will be run using Zoom, and closed captions will be activated on request.

Slides are in PDF format, exercises are in R markdown, and both will be downloadable in advance. If HTML slides and alt text are needed to assist accessibility, we will make every effort to provide these, please let us know well in advance. Lectures will be recorded and uploaded to YouTube, and available for a week following the workshop. Please email Eve (eve.slavich@unsw.edu.au) with any questions or requests.

Prerequisites: We assume knowledge of introductory statistics, including principles of study design, the concept of a p-value, the concept of a confidence interval, one-sample and two-sample t-tests, and the equivalence of a t-test to simple linear regression and simple linear regression (with a single dependent and single independent variable). All of these are covered in our Introduction to Statistics courses.

You will need R and Rstudio installed on your computer. We also assume you have some experience with R. If you are new to R, you should complete our one-day Introduction to R course, May 9 prior to this course. For more details and register HERE.

Also, you will be expected to watch this seminar on Study Design and Statistical Principles below ahead of the course.

Course requirements: You will need to bring and use your own computer during the workshop.

Date: Tuesday 21 to Thursday 23 May 2024

Duration: 9.30am - 4.00pm, each day

Location: K-D26-G07 - Bioscience G07, this workshop will be delivered mainly in person with limited remote access. We highly encourage you to attend in person.

You will receive a certificate of completion for the course.

Note: If you have a funding support and would like to pay by "Internal Funding Payment", please contact us stats.central@unsw.edu.au

FAQs

How can I contact the organiser with any questions?

Please send us an email at: stats.central@unsw.edu.au