Overview
1. Read the data into your chosen software package, and carry out any necessary recoding
(e.g. to deal with the fact that -1 represents a missing value).
2. Carry out an exploratory analysis that will help you to start building a sensible statistical
model to explain and predict the proportion of ‘Leave’ votes in a ward. This analysis
should aim to reduce the number of candidate variables to take into the subsequent
modelling exercise, as well as to identify any important features of the data that may
have some implications for the modelling. You will need to consider the context of the
problem to guide your choice of exploratory analysis. See the ‘Hints’ below for some
ideas.
3. Using your exploratory analysis as a starting point, develop a statistical model that
enables you to predict the proportion of ‘Leave’ votes in a ward, based on (a subset of)
the ward characteristics; and also to understand the variation in proportions of ‘Leave’
votes between different wards. To be convincing, you will need to consider a range of
models and to use an appropriate suite of diagnostics to assess them. Ultimately
however, you are required to recommend a single model that is suitable for
interpretation, and to justify your recommendation. Your chosen model should be either
a linear model, a generalized linear model or a generalized additive model.
4. Use your chosen model to predict the proportion of ‘Leave’ votes for each of the 267
wards with missing voting data, and also to estimate the standard deviation of your
prediction errors.