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 show a workaround to make it (relatively) easy to work with weighted survey data in Power BI, and ruminate on how this compares to other approaches of working with weighted data.
A negative binomial model isn't adequate for modelling the number of people killed per firearm incident in the USA; the real data has more events of one death, and also more extreme values, than the model. But estimating the model was an interesting exercise in fitting a single negative binomial model to two truncated subsets of data.
Two ways of fitting a model to truncated data.
I investigate a question about the Twitter network, and find that generally (until reaching a level of fame few of us aspire to), having more followers oneself is associated with following people who have less followers, not more.
I outline the stats- and data-related books I most enjoyed reading in 2017.
An interactive network graph is a great way to understand a statistical classification standard.
Resolving an apparent conundrum where the mean spend and other value variables seems to be higher for nearly everywhere... an adventure in double counting (individuals contributing to multiple groups' averages).
Reflections on recruiting data scientists for the public sector, which could maybe be used as practical guidance for someone.
I note for future use a couple of things to be aware of in using R to schedule the execution of SQL scripts.
Playing around with polishing graphics, including an animation, of the seasonality of plague deaths in medieval Europe, early modern Europe, and nineteenth century India and China.