Regression Models: A Hands-on Approach using R

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Statistical Services Centre Ltd
Start - End
8 Nov - 9 Nov
Study Options
GBP 420


Regression is a powerful technique for studying relationships between quantitative variables. Summarising relationships by the most appropriate equation (modelling) is very quick when using a statistical package. It is also easy to progress from basic models to more complex situations such as comparison of regressions.

The course introduces commonly-used linear regression techniques using a combination of presentations and computer-based practicals, whereby theory is firmly placed into practice. The R statistical software will be used throughout the course.

The emphasis in this course is on practical data analysis. Examples are drawn from a range of scientific disciplines.

Delivery Mode

All training is online and will be delivered live each day between 09:00 and 17:30 (GMT). Delivery platform: Zoom, which may be freely accessed. Questions may be asked using Zoom's chat box. Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support. During presentations the team member who is not speaking can take questions in addition to the presenter.​

Who Should Attend?

Scientists and related who need to be conversant with the concepts of regression and the process of choosing a model. Participants are assumed to have a working knowledge of:

  • Hypothesis testing
  • Standard errors and confidence intervals
  • The R statistics software.

How You Will Benefit

Participants will acquire an appreciation of how basic linear regression concepts can be easily extended to investigate more complex situations. You will learn how to use the R statistical software to fit linear regression models, interpret R output and report results in non-statistical language. In particular, you be able to describe and use an analysis of variance table, and appreciate the problems associated with model selection.

What Do We Cover?

  • Exploratory stage: modelling relationships
  • Simple linear regression: fitting straight lines and curves
  • Assessing the model assumptions: model-checking using residuals
  • Extensions to the linear model: comparison of regression lines
  • Multiple linear regression and model selection
  • Use of transformations. Case study: use of the logarithmic transformations
  • Quadratic regression models and their application
  • Common issues in multiple linear regression: interpretation of R-squared, correlation versus regression, association versus causation, multicollinearity
  • Use of R for fitting linear regression models.


Practical work will be done in R.

Note: For practical work, participants must download and install a number of CRAN packages in R. This must be done prior to the start of the course.

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