Mar 12 2019
More About the Math of the Process Behavior Chart
In statistics on time series with “moving” in their name, each value is correlated with past and future neighbors — that is, the series is autocorrelated. It affects the way you can use these statistics to detect anomalies and issue alarms.
The moving range in the XmR chart is a case in point. Its autocorrelation in the moving range chart is self-inflicted. It is autocorrelated by construction, regardless of whether the raw data themselves are.
Some raw data are autocorrelated. For example, when you issue a replenishment order for a part by pulling a Kanban from a bin, you are assuming that the demand for a coming period to match that of the period that just elapsed, with minor fluctuations. Implicitly, you are leveraging the autocorrelation of the part consumption across periods.
On the other hand, if a physical characteristic of a manufactured part is the sum of a constant and noise, then the noises are independent, and therefore uncorrelated. Taking moving ranges introduces an autocorrelation between consecutive values that is absent in the raw data.
Dec 25 2021
Always the Hurricanes Blowing
Atlantic hurricanes hurt people and destroy property around the Gulf of Mexico every year. Whether climate change is increasing their frequency is a serious question. Don Wheeler just had a column on this subject in the latest Quality Digest. It’s about Torturing the Data. He argues that we should be careful about not force-fitting models to arrive at pre-ordained conclusions.
His way of not torturing the history of hurricanes in the Atlantic is plotting yearly counts on, what else, an XmR chart. It’s just as he would for hole diameters in metal plates coming off a production line in 1945. Hurricanes and holes in metal plates, however, have different backstories.
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By Michel Baudin • Press clippings • 2 • Tags: Backstory, data science, Hurricane, NOAA, Seasonality, Trend, XmR