Details of Award
NERC Reference : NE/M006026/1
Advanced statistics training
Training Grant Award
- Lead Supervisor:
- Professor M Scott, University of Glasgow, School of Mathematics & Statistics
- Grant held at:
- University of Glasgow, School of Mathematics & Statistics
- Science Area:
- Atmospheric
- Earth
- Freshwater
- Marine
- Terrestrial
- Overall Classification:
- Atmospheric
- ENRIs:
- Biodiversity
- Environmental Risks and Hazards
- Global Change
- Natural Resource Management
- Pollution and Waste
- Science Topics:
- Climate & Climate Change
- Regional & Extreme Weather
- Biogeochemical Cycles
- Statistics & Appl. Probability
- Earth Resources
- Abstract:
- Our highly successful training initiative contains 2 main components: (1) an introductory residential week-long training course giving core statistical training specifically targeted for environmental scientists; and (2) a more advanced week long training course on advanced statistical topics. We will also further develop existing on line resources trialled in the residential course run in January 2014. The first residential course will take participants new or inexperienced to statistics and provide them with a fundamental grounding in both theory and computation, and will introduce them to some advanced statistical modelling topics and ideas. The course can take a maximum of 35 students (over the past 10 years, similar courses we have run have been oversubscribed by 100%, and fully booked within days of the announcement). Online material provides early preparation for the statistical training and students who have attended the course will be able to re-cover some of the material again in their own time to reinforce the skills they have learnt. The second week long training course will then build on the knowledge students have obtained either from the self study material or from the residential course, and will introduce students to advanced statistical topics that are relevant to the NERC remit. We provide high quality (as evidenced by student feedback) training and experience in modelling in environmental sciences, scientific computing, including handling large data sets, data visualisation, quantifying risk and uncertainty. Students receive hands-on training and support from a teaching team with wide environmental experience. This proposal addresses directly the need that Environmental Scientists should understand the power and limitations of leading edge statistical methodologies in advancing the environmental sciences. The two training courses provide a wide ranging introduction and application of statistical methodologies as they can be applied in a wide variety of environmental sciences. The underpinning concept is that of building explanatory, descriptive and predictive models, with an appropriate quantification of uncertainties. The training is delivered making use of R which is a package of immense power covering both analysis and graphics,continually being updated and enhanced by a community of researchers who publish and make freely available packages which deliver the capability of leading edge statistical analysis to a wide user community. Students leave these courses with an improved understanding and ability to perform statistical analyses in their own research, and to interpret the analyses of others. They also learn an appreciation of cutting edge statistical technology. Each training week also involves several guest lecturers from the environmental science communities. Attendees find these lectures inspirational since they observe, first hand, the utility and practicality of many of the statistical techniques they have met.
- NERC Reference:
- NE/M006026/1
- Grant Stage:
- Completed
- Scheme:
- Doctoral Training
- Grant Status:
- Closed
- Programme:
- Advanced Training
This training grant award has a total value of £73,000
FDAB - Financial Details (Award breakdown by headings)
| Total - Other Costs |
|---|
| £73,000 |
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