I am finally getting around to reading The Signal and the Noise, and I was struck by a very bad graph that occurs early in the book. On page 21, the difference between the predicted and actual default rate for … Continue reading
I spent a fairly long time today using the google to try to figure out how to make the labels in a dendrogram bigger. What’s a dendrogram, you ask? This is (assuming you didn’t follow the link in the last … Continue reading
At times, I find I must choose whether to write a blog post or work on manuscripts that are in various stages of development. For obvious reasons, I usually choose to work on manuscripts. Typically, once the manuscripts that I … Continue reading
I updated my Statistics page today. I added links to and descriptions of a number of files that I’ve been meaning to make available for some time. Previously, there was just a single Matlab function for finding maximum likelihood estimates … Continue reading
The awful graph below showed up on facebook recently. It’s old (from 5/3/11), but it struck me immediately how silly it is, and I was curious what the numbers look like when plotted more transparently (i.e., honestly). What’s the problem … Continue reading
As I mentioned previously, I’m working on a paper exploring model identifiability in general recognition theory (GRT; please read the linked post if you haven’t already to get a very brief introduction to GRT – this post won’t make much … Continue reading
I’ve got a long-ish post about general recognition theory (GRT) in the works, and it should really precede this post, but, well, this one’s quick and easy, and the full post on GRT isn’t. So, here’s a very brief introduction … Continue reading
For the past few months, I’ve been working with David Landy on analyzing and writing up a series of number line judgment experiments. Participants in these experiments see very large numbers (e.g., 783,000,000) and respond by indicating the appropriate location on … Continue reading
Suppose you have some three-dimensional categorical data and you’ve come up with some clever way to model the underlying probabilities. Let’s say, for example, that you have data from a categorization experiment in which there are three possible responses to m stimuli, … Continue reading
