I write about applications of data and analytical techniques like statistical modelling and simulation to real-world situations. I show how to access and use data, and provide examples of analytical products and the code that produced them.
I try out biterm topic modelling on a free text question in the 2017 New Zealand Election Study about the most important issue in the election.
I look at some unusual data where the median was higher than the mode, and show how to model it in Stan as a mixture of two negative binomial distributions.
I confront past nowcasts of effective reproduction number for Covid-19 in Victoria with the best hindsight estimate, and confirm that the nowcasts lag change in the 7-14 days leading up to the time they are made.
I have a go at synthesising data to re-create a controversial and much-criticised chart that used ordinary least squares to fit a line relating university subjects' costs per student to the number of students in each subject.
An observational study claiming to be an RCT might have something to say but there are far too many discretionary researcher choices taken to believe its findings. But I use this as a chance to play with statistical inference after estimating a regression via lasso.
Exploration of change in occupations in the Australian health industry, and economy more broadly, from 1986 to the present.
There is a fast growing body of knowledge and tools to help estimate effective reproduction number of an epidemic in real time; I have a go at applying the latest EpiNow2 R package to data for Covid-19 cases in Victoria, Australia.
Science isn't broken, but journals are. A joint solution is emerging for disparate problems of access, quality control and replicability in scientific publishing.
My forecasts for the 2020 New Zealand general election are out, and predict a comfortable win for Jacinda Ardern's Labour Party either alone or in coalition.