Exploring and predicting using linear regression in R (QCIF) [webinar]
This is a QCIF course and places are highly limited as Graduate School is paying the fee. If you are unable to attend, please de-register (or email graduateschool@uq.edu.au if you are unable to do so). An unexplained absence could result in all your future QCIF registrations becoming Waitlisted. You may only attend the workshop once. Repeat bookings will be removed and placed on the Waitlist.
This interactive hands-on 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 includes practical sessions giving hands-on experience with regression analysis in R.
Prerequisites and pre-course preparation
- Prior experience with R and the RStudio interface is required, as the basics of R will not be covered. Consider signing up for the Library's R and RStudio course
- Participants are expected to have a basic familiarity with the concepts of descriptive statistics and elementary statistical hypothesis testing.
- Participants will need to bring a laptop configured for Eduroam wifi access (https://my.uq.edu.au/information-and-services/information-technology/internet-and-wifi/connecting-wifi) and with R and RStudio installed.
About Statistics and modelling
If your research study employs quantitative or mixed methodologies, you would need to understand the nuts and bolts of statistics and modelling. The Graduate School works with different providers to provide a range of sessions covering descriptive and inferential statistics as well as modelling with esteemed providers such as ISSR and Student Services.
Useful links
- Library's 3D modelling workshops
- National statistics at the Australian Bureau of Statistics
- An overview of statistics by Britannica
- ISSR's MFSAS (Methods for Social Analysis and Statistics) courses
- Stats for Research students - Open Textbook Library