This five-day summer school will help you interpret published research and perform basic statistical analysis.
You'll learn research and quantitative skills - this will enable you to make your own judgements about research results and understand their true meanings.
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).
CASC run another summer school on Statistics and Regressions with R: an Introduction.
The course will cover the following topics:
- How to develop a sound research question
- Study design, sampling and confounding
- Types of data
- Graphical displays of data and results
- Summarising numeric and categorical data
- Quantifying differences and associations between groups
- Hypothesis testing
- Confidence intervals and p-values
- Parametric and non-parametric statistical tests
- Introduction to regression
You'll also have the opportunity to attend one-to-one and group tuition sessions on the material taught throughout the week.
As part of the summer school, you can attend a number of free social activities, including an evening reception on the first day, a dinner and a trip to see a musical.
Who this course is for
You don't need any knowledge of statistical analysis to attend this course.
By the end of this course you should be able to:
- set out a plan of analysis for a research question, accounting for all types of data involved and aspects of the question
- choose the best way of graphically displaying data and results
- choose the significance tests suitable to answer a research question
- use results to make inferences about the wider population of interest and answer a research question
You'll receive a certificate of attendance on completing the course.
Cost and concessions
The standard price is £1,500.
The fee includes:
- a welcome pack
- printed course materials
- refreshments and lunches
- social events
The fee doesn't cover accommodation, which you'll need to arrange yourself.
Refunds won't be given for cancellations made within two weeks of the start date or for non-attendance.
Cancellations made within two to four weeks of the start date will incur a fee of 25%. Please send all cancellation requests directly to the course administrator.
Find out about other statistics courses
CASC's stats courses are suitable for anyone requiring an understanding of research methodology and statistical analyses. The courses 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
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.
Dr Dan Green - Course Lead
Dan joined GOS ICH in May 2017 as a Teaching Fellow in CASC. He has a Bachelor’s degree in Mathematics and a Master's degree in Medical Statistics from University of Leicester. He was awarded a NIHR Research Methods Fellowship, hosted at the Arthritis Research UK Primary Care Centre within Keele University. He started a PhD at the same department in 2012, with a NIHR School for Primary Care Research Studentship, exploring hand symptoms over a six year follow-up using latent-related methodology. During his six years at Keele, Dan was involved in numerous applied observational studies and a clinical trial that included a variety of statistical methods, with specialties including latent class analysis (and extensions), STATA programming and survival analysis.
Course information last modified: 06 Mar 2019, 14:33