To be clear, this is not a diss of the article. It is hard to create fake data that look realistic at first glance but are not under statistical process control. That's just how good control charts are at separating signal from noise.

If someone wants to learn more, this is how I put the introduction: https://entropicthoughts.com/statistical-process-control-a-p...

Fantastic stuff, thank you.

Eh depends what you mean I suppose, but a small dense cluster with enormous outliers is not a great sign usually.

Almost nothing has (effectively) unbounded variance, so most things are under statistical control in a sense. With some notable exceptions (earthquakes, any other event with exponentially decreasing frequency and exponentially increasing damage).

For the sake of argument I assumed the author meant that the variance of the thermostat was too high to be practical.

Statistical control is a very specific term: https://entropicthoughts.com/statistical-process-control-a-p..., and one which you'd expect anyone with a significant interest in Deming to understand.

My expectation is that Lorin would read the parent comment and say some variant of "oh, whoops, I didn't check." As the parent noted, it's not really that important to the overall point.

None of those data points are outliers, since they are within the band of what's expected from the process.

Yes, the variability of the thermostat is awful, and the SPC practitioner would care about that. But the key thing is that dealing with bad variation that's in control requires different techniques than dealing with actual out-of-control processes.

Short cycling is bad, most residential systems will look like the second chart.

Especially older buildings where things like uneven sun loading have a bigger impact, and where things like outdoor reset are less common.