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The petrochemical industry is eager to develop advanced fuels which improve fuel efficiency both for economic and environmental reasons. Statistics plays a crucial role in this costly process. Innovative Bayesian methodology developed by Gilmour was applied at Shell Global Solutions to data from fuel experiments to solve a recurring statistical problem. The usefulness of this approach to the wider petrochemical industry has been recognized by the industry-based Coordinating European Council (CEC) for the Development of Performance Tests for Fuels, Lubricants and other Fluids, who in their statistics manual have included Gilmour's method as an alternative to procedures in the ISO 5725 standard.
Small area estimation (SAE) describes the use of Bayesian modelling of survey and administrative data in order to provide estimates of survey responses at a much finer level than is possible from the survey alone. Over the recent past, academic publications have mostly targeted the development of the methodology for SAE using small-scale examples. Only predictions on the basis of realistically sized samples have the potential to impact on governance and our contribution is to fill a niche by delivering such SAEs on a national scale through the use of a scaling method. The impact case study concerns the use of these small area predictions to develop disease-level predictions for some 8,000 GPs in England and so to produce a funding formula for use in primary care that has informed the allocation of billions of pounds of NHS money. The value of the model has been recognised in NHS guidelines. The methodology has begun to have impact in other areas, including the BIS `Skills for Life' survey.
Collaboration between the University of Southampton and scientists at GlaxoSmithKline (GSK) has resulted in the adoption of new statistical design of experiments and modelling methods for the confirmation of a robust operating region for the industrial production of new drugs. These methods have enabled larger numbers of factors to be investigated simultaneously than previously possible, improving scientific understanding of the chemical processes and producing savings of time, money and effort. Southampton's new methods were used in a key process required for the registration of a new skin cancer drug with the US Food and Drug Administration, where the research enabled the verification of a robust operating region to be completed in a third of the previous time.
This study demonstrates how Bayes linear methodologies developed at Durham University have impacted on industrial practice. Two examples are given. The approach has been applied by London Underground Ltd. to the management of bridges, stations and other civil engineering assets, enabling a whole-life strategic approach to maintenance and renewal to reduce costs and increase safety. The approach has won a major award for innovation in engineering and technology. The methodology has also been applied by Unilever and Fera to improve methods of assessing product safety and in particular the risk of chemical ingredients in products causing allergic skin reactions.
Since 2008, statistical research at the University of Bristol has significantly influenced policies, practices and tools aimed at evaluating and promoting the quality of institutional and student learning in the education sector in the UK and internationally. These developments have also spread beyond the education sector and influence the inferential methods employed across government and other sectors. The underpinning research develops methodologies and a much-used suite of associated software packages that allows effective inference from complicated data structures, which are not well-modelled using traditional statistical techniques that assume homogeneity across observational units. The ability to analyse complicated data (such as pupil performance measures when measured alongside school, classroom, context and community factors) has resulted in a significant transformation of government and institutional policies and their practices in the UK, and recommendations in Organisation for Economic Co-operation and Development (OECD) policy documents. These techniques for transforming complex data into useful evidence are well-used across the UK civil service, with consequent policy shifts in areas such as higher education admissions and the REF2014 equality and diversity criteria.
Members of the Work, Interaction and Technology (WIT) Research Centre, King's College London have had a significant impact on the ways in which a number of global corporations and other major organisations design, deploy and evaluate advanced systems. They have developed innovative video research methods that have provided critical resources for organisations, including Hitachi, Xerox, BT, Microsoft, and London Underground, to undertake fine-grained analysis of work, communication and technology in complex organisational environments. Their methods and approach have formed the foundation to a range of more applied `interventions' in areas that include health care, transport, education, markets and the cultural industries.
Our research has been applied directly by Aviva plc. to develop improved products in the general insurance market (e.g. household and car) and in the more specialised area of enhanced pension annuities. As a result, Aviva has become more competitive in these markets and customers are enjoying better value for money. In the case of enhanced annuities, the benefits are in the form of higher pension income for those accurately identified as facing shortened life expectancies. Aviva is the largest insurance company in the UK and the sixth largest in the world.
Successful planning in Scotland requires a set of geographical units for which data can be collected and analysed. Researchers at St Andrews have developed a new `small area' geography for Scotland. `Data zones' (DZs) provide a scientifically-based template for data mapping and has been adopted as the default geography used by public and private organisations to display and analyse data on topics as diverse as economic planning, health, education and transport, thus impacting how and where policy is enacted. To be statistically appropriate these units have to be compact, homogenous, with approximately the same size population and publically acceptable. This is not a trivial task, involving millions of potentially different solutions. In 2001, Scottish Neighbourhood Statistics (SNS) commissioned St Andrews to study how such units should be defined and to develop a methodology for creating them. Using the experience and skills developed over many years working in this area, the team developed a methodology and established the official small area geography of Scotland.
Research conducted at Essex as part of the British Election Study (BES) successfully demonstrated the validity of internet-polling methods. This research has been integral to the development of YouGov and the growth of the online-survey sector in the UK. In addition, the BES has: informed public opinion about the likely consequences of introducing AV in UK elections; led to the creation of a spin-out partnership, BPIX, which conducts polls for major national newspapers; informed parliamentary debates about the causes of non-voting; and served as a template for national election studies in other European countries.
Research at the University of Manchester (UoM) has developed new approaches, methods and algorithms to improve the statistical confidentiality practices of data stewardship organisations (DSOs), such as the UK's Office for National Statistics. The research and its products have had significant impacts on data dissemination practice, both in the UK and internationally, and have been adopted by national statistical agencies, government departments and private companies. The primary beneficiaries of this work are DSOs, who are able to both disseminate useful data products, and protect respondent confidentiality more effectively. Secondary beneficiaries are respondents, whose confidentiality is better protected, and the research community, as without `gold standard' disclosure risk analysis, data holders can be overcautious.