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.
Rents in Melbourne have on average grown fastest in suburbs that were the cheapest in 2000; at least for two and three bedroom flats and for two bedroom houses. Also, scatterplots are awesome.
Simulating complex survey data in order to fit slightly mis-specified relative risk models, we find that confidence intervals' coverage is pretty much as advertised if we use appropriate methods that adjust for the complex survey data, but under-perform if the data is treated naively as coming from a simple random sample.
Explanation and demonstration with simulated data of the difference between relative risk ratios and odds ratios, and how to extract them from a generalized linear model.
I look into whether the regional fuel tax in Auckland has led to changes in fuel prices in other regions of New Zealand.
A demo of a favourite combination of multiple imputation, bootstrap and elastic net regularization. I look at what are good leading indicators, with reliable data available, of New Zealand's economic growth. The results turn out to be last quarter's economic growth; food prices; visitor arrivals; car registrations; business confidence; and electronic card transactions.
I have a brief look at the relationship between reported business confidence in New Zealand and what actually happens down the track with economic growth. Confidence can help (a bit) explain future growth; but current and past growth isn't helpful in explaining confidence.
A few months back, I set up a server on Amazon Web Services with a data sciencey toolkit on it. Amongst other things, this means I can collect data around the clock when necessary, as well as host my little RRobot twitter bot, without having a physical machine humming in my living room. There are lots of fiddly things to sort out to make such a setup actually fit for purpose.
I explore the relationship between household income and expenditure on gasoline and motor oil in the USA Bureau of Labor Statistics' Consumer Expenditure Survey.
Simulating a population with changing total fertility rate, life expectancy, infant mortality, and other parameters
Minor updates available on CRAN for the ggseas (seasonal adjustment on the fly) and Tcomp (tourism forecasting competition data) R packages