This 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 if you are unable to do so). An unexplained absence could result in all your future QCIF registrations becoming Waitlisted.

Recommended Participants

Researchers who wish to expand their skills into regression methods and who are considering using regression approaches in their research. The workshop is applicable for all disciplines, although examples and exercises will be based around biological datasets.

Prior expertise with SPSS is required to a level equivalent of that provided by the “Introduction to SPSS” workshop, as the basics of SPSS will not be covered. Participants are expected to have a basic familiarity with the concepts of descriptive statistics and elementary statistical hypothesis testing.

Learning Objectives

  • Understand the principles of linear regression methods
  • Identify the appropriate correlation or regression analysis for a dataset
  • Carry out regression analysis using SPSS
  • Interpret and report on the results of that analysis


  • 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

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