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This short course focuses on understanding the principles of logistic regression using the notions of odds, odds ratios and transformations.
It includes discussion of how good the given model is, and ways of improving it.
This course takes place online, over two mornings (9:30am to 1pm). There is also a self-paced, online version of this course that you can register for and start at any time.
This course is delivered by UCL's Centre for Applied Statistics Courses (CASC), part of the UCL Great Ormond Street Institute of Child Health (ICH).
Binary (proportion/percentage) outcomes are common in medical and scientific research. However, such outcomes can't be validly analysed using basic linear regression analysis.
It's important to understand how to analyse binary outcomes appropriately to ensure you can draw useful and valid conclusions from the data.
The course covers the following key topics:
- Odds ratios as a means of comparing binary outcomes between two groups
- How logistic regression allows for other factors within this comparison
- The basics of logistic regression
- Model selection and goodness-of-fit with applied examples
- Interpretation of SPSS output
- Discussion of extension to the analysis of ordinal outcomes
By the end of the course you should be able to:
- understand when it is relevant to choose logistic regression
- understand the use of odds, odds ratios and transformations in logistic regression
- correctly interpret the results of logistic regression
- choose the best logistic model that describes the relationship under question
- understand how logistic regression can be extended for nominal and ordinal outcomes
Cost and concessions
The fees are as follows:
- External delegates (non UCL) - £150
- UCL staff, students, alumni - £75*
- ICH / GOSH staff and doctoral students - free
* valid UCL email address and/or UCL alumni number required upon registration
You can request a certificate of attendance for this course once you've completed it. Please send your request to email@example.com
Include the following in your email:
- the name of the completed course for which you'd like a certificate
- how you'd like your name presented on the certificate (if the name/format differs from the details you gave during registration)
Find out about other statistics courses
CASC's stats courses are for anyone requiring an understanding of research methodology and statistical analyses. The courses will allow non-statisticians to interpret published research and/or undertake their own research studies.
Find out more about CASC's full range of statistics courses, and the continuing statistics training scheme (book six one-day courses and get a seventh free.)
Dr Chibueze Ogbonnaya
Since joining the teaching team at CASC in February 2019, Chibueze has contributed to the teaching and development of short courses. He currently leads and co-leads short courses on MATLAB, missing data, regression analysis and survival analysis. Chibueze has a BSc in Statistics from the University of Nigeria, where he briefly worked as a teaching assistant after graduation. He then moved to the University of Nottingham for his MSc and PhD in Statistics. His research interests include functional data analysis, applied machine learning and distribution theory.
Dr Eirini Koutoumanou
Eirini has a BSc in Statistics from Athens University of Economics and Business and an MSc in Statistics from Lancaster University (funded by the Engineering and Physical Sciences Research Council). She joined UCL GOS Institute of Child Health in 2008 to develop a range of short courses for anyone interested in learning new statistical skills. Soon after, CASC was born. In 2014, she was promoted to Senior Teaching Fellow. In 2019, she successfully passed her PhD viva on the topic of Copula models and their application within paediatric data. Since early 2020 she has been co-directing CASC with its founder, Emeritus Professor Angie Wade, and has been the sole Director of CASC since January 2022.
"Extremely well presented course, pace was good, presenter made sure we all understood."
"The lecturer was truly excellent. Very informative and enjoyable course."
"A well-presented informative course with a good emphasis on understanding the concepts before attempting analysis. Overall a great course that I would recommend."
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Course information last modified: 19 Apr 2022, 10:09