PedRIO seminar: Getting further with data analysis faster and easier
  • Room 117, Rolle Building, Plymouth University

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This seminar is facilitated by Chris Wild from the University of Auckland, New Zealand.

Chris will talk about interests that have the common theme of finding ways to make statistical data analysis more accessible to more people and enable them to get further into the world of data faster and painlessly. This links software projects, research projects and teaching.

The most developed teaching project is a MOOC (online open course, but not so massive) called Data to Insight, an introduction to statistical data analysis, which ran on the UK’s FutureLearn platform late last year and repeats starting 19 October this year. Course completers ranged from members of a small high-school physics honours class in up state New York to PhD researchers from many areas, and from journalists, linguists and arts administrators to economists, data managers, marketers and scientists. Data to Insight and the software projects (iNZight and VIT) leverage visualisation as a primary discovery and communication channel.

A by-product of this work is an online visualisation and analysis system called iNZight Lite (currently in beta) which will very soon have capabilities all the way to the analysis of data from complex surveys (for example, cluster sampling within strata and unequal selection probabilities) via generalised linear models. This opens up possibilities for people who are interested in outreach and dissemination. For example, a simple link like http://programAddress?url=DataFileAddress instantly adds substantial data analysis capabilities to any other webpage or online system.

We are also working on invisibly bringing in data from confidential databases providing an inexpensive way of experimenting with what you can/should/might-want-to disseminate online in terms of providing visualisation and analysis tools to people outside the trusted-researcher groups who are allowed to get their hands on confidential data.

Chris is particularly interested in finding people and projects where collaboration might be mutually beneficial.

This event is open to anyone with an interest in data analysis but booking is required (via the above link) to attend. 


Chris Wild is a Professor of Statistics at the University of Auckland, the birthplace of the statistical system R. His methodological interests are in statistical modelling in response-selective and missing data problems, and his statistics education interests are in statistical thinking and reasoning processes, data visualisation and concept visualisation.

For further information please contact Debbie White at:

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