UCL Great Ormond Street Institute of Child Health


Great Ormond Street Institute of Child Health


Introduction to Regression Analysis

16 April 2024–18 April 2024, 9:30 am–5:00 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. The optional workshops allows delegates hands-on use of a statistical package (SPSS/ R/ Stata) to see how the theory can be applied to answer a specific research question.

Event Information

Open to







Centre for Applied Statistics Courses


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NOTE: This course will be delivered in hybrid mode, i.e. the in-person lecture will be transmitted live on zoom. The face-to-face part of the hybrid courses will go ahead conditional on at least 5 participants registering for this mode of delivery. Few classes may be delivered exclusively online, so please check under the Future dates tab for clarification. The online part of the class will be supported by its own dedicated teaching staff (along with the lead lecturer), therefore participants can expect the same level of group and individual support as the face-to-face class. You will be able to choose your preferred mode of attendance (face-to-face or online) during registration.

Additionally, we now offer this as a self-paced, online course that you can register for and start at any time. Click below under the 'Online/Self-Paced Materials' tab to register and gain access:


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.

In the optional workshop, an example dataset will be introduced, regression models will be run through SPSS/R/Stata 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 optional workshop 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 the second part of the course is taught using specific packages such as SPSS, R or Stata, the theory taught throughout this course is applicable to other statistical packages. If attending this optional workshop, a basic understanding of the statistical package is desirable (offered in our 'Introduction to SPSS', 'Introduction to R' and 'Introduction to Stata' courses).

Everyone wishing to attend the optional workshop should ensure the software is installed and licensed on their computer. Where possible, we recommend using a recent version of SPSS/R/Stata for maximum compatibility with the notes provided during the course.


Course Fees (live)

Below are the course fees for all courses delivered live either face-to-face, or through online video feed.

External Delegates (Non-UCL)

£150.00 (£275 for both parts)

UCL Staff, Students, Alumni

£75.00* (£137.50 for both parts)

Staff and Doctoral Students from ICH/GOSH

FREE †    

† Limited free spaces available. If there are no free places remaining, Staff and Doctoral Students from ICH/GOSH can still register at the UCL rate.

* Valid UCL email address and/or UCL alumni number required upon registration. Please note, this category does not include hospital staff unless you hold an official contract with the university.

CancellationsCancellation Policy

Course Fees (self-paced)

Below are the fees for access to the online, self-paced version of this course.

External Delegates (Non-UCL)


UCL Staff, Students, Alumni *


Staff and Doctoral Students from ICH/GOSH *

FREE †    

* A valid UCL email address and/or UCL alumni number required. Please note, this category does not include hospital staff unless you have a formal affiliation with the university. To get this discount, please email ich.statscou@ucl.ac.uk to confirm your eligibility and receive a code that can be entered at the checkout.

† If you are a doctoral student from SLMS (School of Life and Medical Sciences), you can get access to this, and all other self-paced courses developed by CASC by filling in this online form.

Future Dates
: Main Course Only9.30am - 5.00pm Subcribe to our Mailing List
: Plus Optional SPSS workshop9.30am - 5.00pm Subscribe to our Mailing List
: Plus Optional Stata workshop9.30am - 1.00pmSubscribe to our Mailing List
18th of April 2024: Plus Optional R workshop1.30pm - 5.00pm Subscribe to our Mailing List
‡ For those attending online, we recommend logging in 10 minutes prior to the scheduled start time to access materials and ensure the course can start promptly at 1.30pm. (for most courses this will be 9.30 but for meta is 1.30 this time)
Online/self-paced materials

An online, self-paced version of this course is available that includes the following materials:

  • Full electronic notes
  • Short lecture videos (recorded outside of the classroom) that follow closely with the notes
  • Interactive multiple choice quizzes
  • Extended practical exercises (with solutions) for further comprehension

Support: The course includes unlimited support through a forum that will be manned by one of our teaching fellows. We aim to provide responses to all questions on Tuesday and Friday each working week. Note that questions can only relate directly to the course materials and should not be used as a consultancy service for your own projects.

Personalised certificates will be generated on completion.

UCL extend: The link below leads to the UCL extend store where this self-paced course is available for purchase. If you are eligible for a discount, then please email ich.statscou@ucl.ac.uk to receive a voucher code that can be used on checkout. UCL delegates should use their university email address to register, and external delegates can use any other account. 

Click to register (online, self-paced)

Alternatively, if you are a doctoral student from SLMS (School of Life and Medical Sciences), you can get access to this, and all other self-paced courses developed by CASC by filling in this online form.


"Thought it was an excellent course, really helpful to have somethings I have had to teach myself from the internet explained by someone with an actual solid grounding in statistics. Also very useful to have some common misuses cleared up (e.g. use of multivariate when they mean multiple/multivariable and the simple regression to select predictors for a multiple regression model technique). Hard to speak for everyone but pitched at the perfect level for me."

"The presenter was very good and very engaging. confident in the topic she was teaching. I really liked the interaction asking questions throughout the sessions and practical work which helps consolidate the learning."

"I thought this course was perfect; I was worried I would get lost but it was pitched at the right level for me. The instructor was very clear and approachable, which made the course very interactive. Even with all the discussions she still managed to cover the whole set of notes and finished just in time"

"Really enjoyed the course, which is something I didn't think I would ever say about statistics. Originally signed up as I was trying to do multiple linear regression and there were certain aspects that I couldn't quite get my head around. The course was perfectly pitched for my level of previous understanding and covered exactly the sort of thing I was looking for (the practical example was essentially the same as my research but just with slightly different predictors - so ideal). The lecturer was excellent, explained things really well, engaged everyone in the room, and kept it interesting throughout. Days were just the right length and practical exercises and breaks were well spaced to stop people zoning out. Overall a massive help."

"I found the course very helpful. As a nurse who has done one stats module for an Mres I didn't find it intimidating and very accessible . The lecturer was delightful, I will definitely recommend the course to my fellow PhD students"

"Outstanding quality, excellent preparation"

"Teacher was very good, it was clear that she had an in depth understanding of the course content and was able to explain it in a very clear and consistent way. Best stats teaching I've had to date."

"This was a very good course, good value and excellent teaching."

"The course was very well presented and delivered. The course materials were excellent. [The teacher] was very competent and approachable. It was an excellent idea to focus on the theory and not get embroiled in software. Unlike other courses I really appreciated the focus on interpretation of results.