Category: Data Science

John Cook has some thoughts on an interesting problem:

Suppose you want to determine how to price a product and you initially don’t know what the market is willing to pay. This post outlines some of the things you might think about, and how Bayesian modeling might help.

This post is not the final word on the subject, or even my final word on the subject. It is essentially a reply to a friend’s question turned into a blog post rather than an email.

This is a really interesting problem. Price is ultimately a signal rather than the solution. This is an attempt to understand how you start to build up enough information to start setting effective prices. Over a long enough timeframe, we’ll eventually land on a commonly accepted price but that’s an economist’s way of hand-waving away the process.

Sebastian Sauer has a tutorial for us:

A residual is defined as

ri=yi−Xi^βri=yi−Xiβ^.

That is, a residual is a tangible thing in the sense that it describes observables (cf. Gelman 2021, chap. 11.3, p. 161). That is, the residuals are the difference between observed and predicted values.

Read on for the concept of error terms and how we might plot them.

Angus Croll embraces the power of control charts:

Technically, “performance” metrics are those relating to the responsiveness or latency of the app, including start up time.

But TV devices also tend to be more memory constrained than other devices, and as such are more liable to crash during a memory spike — so for Netflix TV we actually care about memory at least as much as performance, maybe more so.

At Netflix the term “performance” usually encompasses both performance metrics (in the strict meaning) and memory metrics, and that’s how we’re using the term here.

Click through for a primer on how they use changepoint and anomaly detection to do this.

Tori Tompkins has a category for us:

Model drift refers to the decline of model performance due to changes in data and relationships. Most drift is caused by things entirely out of our control so while we can’t stop it from happening, we can identify and mitigate it.

Click through for four common explanations for model drift and what you can do about them.

Antoine Soetewey has a handy chart for us:

Being a teaching assistant in statistics for students with diverse backgrounds, I have the chance to see what is globally not well understood by students.

I have realized that it is usually not a problem for students to do a specific statistical test when they are told which one to use (as long as they have good resources and they have been attentive during classes, of course). However, it appears that the task is much more difficult for them when they need to choose what test to do.

Click through for the chart, as well as a PDF version. H/T R-Bloggers.