Cost: £150 *
*Concessions may be available
We don't have a date for this course yet. Please contact Patricia John to register your interest.
This one-day course will introduce you to regression methods for analysing count or rate data.
You'll learn about:
- poisson regression - used to determine associations between a count/rate outcome and a number of predictor variables
- quasi poisson and negative binomial regressions - more flexible extensions of the poisson regression
You should have a basic understanding of statistical concepts such as p-values and confidence intervals before attending this course.
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).
When dealing with a count or rate outcome variable, basic linear regression is inappropriate as it will lead to invalid predictions.
Poisson regression is an extension of the ideas used in linear regression that allows counts or rates to be validly modelled against chosen predictor variables.
You'll explore how poisson regression models are formulated, interpreted and checked using worked examples.
You'll learn how poisson regression can be extended when the strict assumptions are not met, by using quasi poisson and negative binomial models.
This course doesn't involve hands-on use of a statistical package. You'll be given results in the notes, which should be comprehensible regardless of which statistical package you are used to.
By the end of the course you should be able to:
- understand what poisson regression is and when to use it
- formulate and interpret poisson regression models for counts and/or rates
- know the diagnostic statistics and assumptions that must be checked to ensure poisson models are valid
- know when to consider quasipoisson and negative binomial models and how to interpret them
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).
You can request a certificate of attendance for this course once you've completed it. Please send your request to firstname.lastname@example.org
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)
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.)
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Professor Angie Wade - Course Lead
Angie, Director of the Centre for Applied Statistics Courses (CASC), has been involved in the provision of statistical training to medically based non-statisticians for 25 years. She has a first class Bachelor’s degree in Mathematics and a Master's degree in Applied Statistics from UCL, and a PhD Statistics in the development of likelihood based models for the construction of covariate-related centiles. She joined GOS ICH in 1990, began the development of CASC in 2008 and was ICH’s first teaching professor. She's an active member of local teaching committees, previously chaired an MSc examination board and is currently a member of the UCL Academic Board Education Committee.
Course information last modified: 10 Jan 2019, 13:58