16 November 2020–19 November 2020, 9:30 am–1:00 pm
This course provides an overview of different regression types and details the application of multiple linear regression. The main course focuses on the theory behind regression analysis, in particular linear regression, and covers the formulation, interpretation and validation of linear regression models. A second, optional session allows delegates hands-on use of a statistical package (SPSS) to see how the theory can be applied to answer a specific research question.
NOTE: Due to the coronavirus outbreak, all courses will now be delivered online through a live video feed. You can expect the same level of group and individual support as you would have received in our face-to-face courses.
Often in research it is important to look at what factors (usually more than one) are associated with, or predict a particular outcome, while also being able to control for the influence of other variables. Regression analysis is a very powerful technique that allows investigation of the combined associations between one or more predictors and an outcome. Some examples where this is helpful are:
i) Within a trial we may wish to adjust for factors that differ between treatment groups to gauge the true effect of treatment
ii) In observational studies we might want to take into account differences between the demographics or health behaviours of two or more subgroups
iii) Considering the combined effects of different factors may facilitate understanding of variation in outcome
The main course focuses on the theory behind regression analysis; starting by introducing different types of regression analysis and how we choose between them, then focusing on linear regression models. Delegates will see how linear regression models are formulated, interpreted and finally how they are checked. Interaction terms, diagnostic tests and model assumptions are all introduced and explained.
On the second optional session, an example dataset will be introduced, regression models will be run through SPSS and used to answer a research question of interest. Delegates will see how the theory taught on the first day can be applied to data to answer important question. This analysis will cover simple and multiple linear regressions; we will interpret the output to determine the best model for the problem and assess the goodness-of-fit via examination of residuals and outlying measurements. The dataset will be available to all delegates whether they choose to attend the second day or not so they can work through the analysis on different statistical packages.
Please note that this course assumes a basic understanding of statistical concepts such as p-values and confidence intervals. Although session two of the course takes place on SPSS, the theory taught throughout this course is applicable to other statistical packages. If attending session two of the course, basic understanding of SPSS is desirable (offered in our one-day 'Introduction to SPSS' course).
Everyone wishing to attend the second day should ensure the software is installed and licensed on their computer. Where possible, we recommend using a recent version of SPSS for maximum compatibility with the notes provided during the course.