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Logistic Regression: an Introduction

  • 5 hours
  • 1 day

Overview

This one-day 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 is delivered by UCL's Centre for Applied Statistics Courses (CASC) - part of the UCL Great Ormond Street Institute of Child Health (ICH).

Course content

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

Learning 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 students - free    

* valid UCL email address and/or UCL alumni number required upon registration

Prices include printed course materials, refreshments (and lunch for non-ICH  participants).

Certificates

You can request a certificate of attendance for this course once you've completed it. Please send your request to ich.statscou@ucl.ac.uk

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)

Cancellations

We accept cancellations up to five working days before the start of the course with a full refund, though we'd appreciate as much notice as possible to re-allocate the place. Places cancelled or changed after this point won't be eligible for a refund. Please send all cancellation requests directly to the course administrator

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.)

Course team

Eirini Koutoumanou - Course Lead

Eirini joined GOS ICH in 2008 as the first CASC Teaching Fellow and was promoted to Senior Teaching Fellow in 2014. She has a Bachelor’s degree in Statistics from the Athens University of Economics and Business and a Master's degree in Statistics from Lancaster University, and she's currently studying for a PhD at ICH. Eirini started teaching at ICH straight after her student days, putting into practice and further developing her passion for statistics teaching. She's played an instrumental role in the formation of CASC and hopes to see it develop further.


Student review

“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.”


Course information last modified: 11 Jan 2019, 09:22

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