The Department is running another series of statistics workshops in Dunedin in 2018 designed mostly for University postgraduates and staff. For some workshops a limited number of places may be available for external participants.
Update There are currently no further workshops planned for this year. Future workshops will be posted on this webpage and notified to all staff and postgraduates via the University's “all departments” email list.
7 to 9 February 2018 (Tilman Davies)
R is the world’s most popular computing language for data exploration and statistical modelling. This is offset to some extent by its rather steep learning curve, so before you even begin summarising your data, it's essential to be comfortable with the language and the virtual structures you’ll utilise in your research pursuits. Even if you have no programming experience and little more than a basic grounding in mathematics, this full 3-day workshop aims to get you comfortable with R as a programming environment, by understanding key commands and the treatment of important objects. The workshop is based on the coordinator’s recently released book The Book of R a copy of which each attendee receives as part of their registration. More information about the book (opens in new tab). Parts I and II will be the main focus in the workshop, with additional/replacement topics possible as per audience requests.
Registration now closed
6-8 June 2018 (Austina Clark and Ting Wang)
In practice, when we have a dataset, the main question is often about which model we should use to analyse the data. This workshop focuses on explaining how to choose suitable models for a dataset, how to carry out basic statistical modelling in R, how to carry out model selection and model diagnostics, and how to interpret the results. This 3-day workshop assumes all participants having a working knowledge of R and basic statistics. If not, we recommend you become familiar with R before attending this workshop. The topics covered include exploratory data analysis, simple linear regression, multiple linear regression (including collinearity), linear logistic model for binomial data, Poisson regression for count data, experimental designs, model selection and model-checking.
Note: This workshop will only run if 10 or more people have registered and paid.
Registration now closed