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for the workshop Simulations and modelling (in R) 
Simulations are an important tool in study design. They allow you to explore different sample sizes and sampling strategies, and see the kind of data and the quality of the results you can expect. Should you use pure random or systematic random sampling? Should you focus on a few study sites, or spread your effort thinly over many sites? Will you be able to answer your research question with the resources available? And, of course, are you clear about how the data will be analysed? To generate simulated data, we need models: "Statistical models are stories about how the data came to be."^{1} You will have to think through how the data you expect to record might be generated, which will depend on what is really happening and how you make your observations. Putting these ideas into a mathematical form to generate data is a good test of how well you understand your model. The R programming language is ideal for building models and simulating data, as well as conducting most kinds of analysis. We'll be using lots of R during the workshop, and participants will get more out of it if they are already familiar with R. The topics to be covered will include:
We can include additional topics depending on the interests of participants, subject to one constraint: it's very easy to devise a simulation that takes hours to run; not a problem if this is your own project  let it run overnight  but unworkable in a workshop like this. Please note that this is NOT a basic statistics workshop: we expect people to be already familiar with the analysis methods we'll be using. If you want a stats workshop come to a Boot Camp! All participants should come with a laptop computer with R and a spreadsheet package installed. 1. Dave Harris, http://www.noamross.net/blog/2013/6/17/harrisbbmle.html 

Page updated 21 Nov 2015 by Mike Meredith 