Introduction to Linear and Logistic Regression Models
Linear and logistic regression models are essential tools for quantifying the relationship between outcomes and exposures. Understanding the mathematics behind these models and being able to apply them allows students to comprehend the results presented in research papers and interrogate their own data. These models also form the building blocks for more advanced statistical techniques taught in other short courses offered by Bristol Medical School. The tutors of this course have extensive experience teaching applied statistics to a wide range of healthcare researchers, both clinical and non-clinical, using real-world data in demonstrations.
| Dates | 3 - 7 March 2025 |
|---|---|
| Fee | £1,100 |
| Format | Online |
| Audience | Open to all applicants (prerequisites apply) |
Course profile
This course aims to provide an understanding of the statistical principles behind, and the practical application of, univariable and multivariable linear and logistic regression in medical, epidemiological and health services research.
Please click on the sections below for more information.
Structure
This 5-day course will be online and consist of a mix of live and pre-recorded sessions and practical work. Over the five days there will be approximately 14 hours of lectures and 15 hours of practicals.
Intended Learning Objectives
By the end of the course participants should:
- have a thorough conceptual understanding of linear and logistic regression;
- appreciate the common threads running through these methods, including stratified analysis, different options for handling explanatory variables, and concepts such as confounding and interaction;
- have a working knowledge of the Stata or R commands to run these models, and a thorough understanding of the output generated from such a package; and
- know the basis on which analytical strategy and model choice is made, and how the results should be interpreted.
Target audience
The course is intended for people analysing data from medical, epidemiological, and health services research, who have used simple methods such as t-tests and chi-square tests, and who now wish to use multivariable methods to control confounding, accommodate interaction, and increase statistical power.
Outline
This course will cover:
1. revision of basic methods in a statistical modelling framework;
2. terminology in regression modelling;
3. indicator variables to incorporate categorical explanatory variables;
4. univariable and multivariable regression;
5. common features of linear and logistic regression models;
6. interpretation of model coefficients as differences in means or odds ratios;
7. adjustment for confounding and exploring effect modification (interaction) in multivariable regression;
8. comparing models with Wald tests and likelihood ratio tests;
9. model assumptions; and
10. analytical strategies.
Teaching staff
This course is taught by a team of experienced statisticians from Population Health Sciences who have taught on this and other short courses at Bristol Medical School.
Prerequisites
To make sure the course is suitable for you and you will benefit from attending, please ensure you meet the following prerequisites before booking:
| Knowledge | You should have knowledge of statistical methods and their implementation in Stata of at least the level achieved in the Introduction to Statistics short course or in R of at least the level achieved in the Introduction to R short course. |
|---|---|
| Software |
You must have Stata (version 15 or higher)* installed in advance of the course. *Internal University of Bristol participants are given access to Stata. Go to Stata Installation Instructions (internal only) for help setting it up before the start of the course. External participants are responsible for providing their own access to Stata, however if you are an employee of a university or another institution you may be able to get a short term free Evaluate license. If you are a student, Stata offer a short term free Student licence (one week). For those who would like to work with R during the practical sessions, we will be using Posit Cloud as an interface for R. You can use your own desktop version of R, if you are already familiar/comfortable with this, or we will provide a link to Posit Cloud. Go to R Installation Instructions for further information. |
| Recommendation | You may find it helpful to have access to two screens - or ability to print materials in advance - in order to run analyses while having course materials open. This is not essential, however. |
Bookings
Before booking this course, please make sure you read the information provided above about the target audience and prerequisites. It is important that you have access to the relevant IT resources needed for the course and meet the knowledge prerequisites to ensure you can get the most from the course.
Bookings are taken via our online booking system, for which you must register an account. To check if you are eligible for free or discounted courses please see our fees and voucher packs page. All bookings are subject to our terms & conditions, which can be read in full here.
For help and support with booking a course refer to our booking information page, FAQs or feel free to contact us directly. For available payment options please see: How to pay your short course fees.
Course materials
Participants are granted access to our virtual learning platform (Blackboard) 1 to 2 weeks in advance of the course. This allows time for any pre-course work to be completed and to familiarise with the platform.
To gain the most from the course, we recommend that you attend in full and participate in all interactive components. We endeavour to record all live lecture sessions and upload these to the online learning environment within 24 hours. This allows course participants to review these sessions at leisure and revisit them multiple times. Please note that we do not record breakout sessions.
All course participants retain access to the online learning materials and recordings for 3 months after the course.
University of Bristol staff and postgraduate students who do not wish to attend the full course may instead register for access to the 'Materials & Recordings' version of this course: Further information and bookings.
Testimonials
100% of attendees recommend this course*.
*Attendee feedback from 2025.
Here is a sample of feedback from the last run of the course:
"The course tutor was excellent. Made extremely challenging material as comprehensible as I imagine it could be. Very gentle and friendly manner, complemented by a wealth of experience and knowledge." - course feedback, March 2025
"Loved the practicals!" - course feedback, March 2025
"Stephanie was great and very attentive. She answered questions and made concepts really clear and always checked for the groups understanding." - course feedback, March 2025
"Stephanie is an excellent teacher with a great style being able to communicate complex ideas in a way that they can be understood by those less knowledgeable and creates a warm and friendly learning environment. I have gained a much deeper understanding of regression and now have a guide for how to approach my own analysis with confidence." - course feedback, March 2025
"Stephanie is a brilliant teacher for difficult to explain concepts. I particularly liked the R based materials and felt they were set up well to ensure that the solutions were available in case I got stuck on a particular question." - course feedback, March 2025
"I could clear up my doubts regarding the basics of linear and logistic regression. I can confidently say that I could construct a regression model with my current level of understanding." - course feedback, March 2025
"Good combination of live and prerecorded lectures and practicals." - course feedback, March 2025
"Lectures were well taught, appreciated the recap sessions at the start of each day and the recap of the whole week on the Friday. I liked that the final sessions were pre-recorded to allow for flexibility in the schedule." - course feedback, March 2025
"I thought the course was pitched at just the right level for me. It started fairly slowly, to build confidence, and then moved to more complex topics towards the end." - course feedback, March 2025
"Just really useful to have a broad foundation of understanding of both linear and logistic regression!" - course feedback, March 2025
"This course was a brilliant refresher for me and I in particular found learning the techniques and codes in R very helpful." - course feedback, March 2025
Bookings for this course have now closed
Stephanie is an excellent teacher with a great style being able to communicate complex ideas in a way that they can be understood by those less knowledgeable and creates a warm and friendly learning environment. I have gained a much deeper understanding of regression and now have a guide for how to approach my own analysis with confidence.
Can't attend live? Just want a refresher?
For University of Bristol staff and postgraduate researchers: access to course materials and lecture recordings for self-paced learning. Find out more.
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