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.
Surgisphere, a tiny startup that claims to be providing large real world data for scientific health studies, is probably fabricating data at scale.
It turns out to be quite easy in R to reorder your bars within each clump, to produce a bad bar chart like the unfortunate example from Georgia doing the rounds.
Even when you adjust for test-positivity rates, the number of new COVID-19 cases per day in Texas is going up, although not as rapidly as the unadjusted numbers imply.
I look at several different ways of accounting for the information given us by high positive testing rates for COVID-19 and look at the impact on estimates of effective reproduction number at a point in time.
A pragmatic way of generating prediction intervals from a generalized linear model with a quasi-likelihood response, if you're prepared to make an additional assumption about the distribution of the response.
I demonstrate the power of the transformation functionality in the scales R package by re-creating an eccentric Fox News chart.
I have a go at quantifying how important different demographic profiles will be for country average case fatality rates for COVID-19.
I have a quick look at how the observed case fatality rate of COVID-19 has evolved over time so far.
I release an improved and updated version of my crosstab webtool for exploring the New Zealand Election Study data, now covering 2017 as well as 2014, and letting the user explore relationship between party vote and a range of attitudes, experiences and demographics.
I check the robustness of last week's analysis of height -> weight by trying a different method of specifying and fitting the model, and checking to see if socioeconomic status is acting as a confounder (because better-off people are both taller and healthier).