Jan 9 2022
Always the Hurricanes Blowing (Part 2)
This post and the previous one use Atlantic hurricanes as a vehicle to show what various visualizations can do. It’s not about second-guessing the data scientists at NOAA who have produced similar displays and much deeper analyses. The point is to show tools anyone can apply to data that may have nothing to do with hurricanes:
- Processes, for spaghetti mapping.
- A fleet of trucks and their freight, on a map.
- Individual workpieces or part containers on a shop floor, if tracked.
- The migration of sources of defects in a manufacturing process.
- Projects going through phases.
- …
While the previous post aimed to show richer visualizations than possible with 100-year old techniques but it was still limited to a few static displays. This means charts that look the same in print and on a screen. This post includes dynamic displays, with animation and interactivity, that you can only use on a screen, and analyses of more of the columns in the HURDAT2 database.
The technology I used to produce these charts takes work but didn’t cost me a dime in license fees. The resulting charts are trivially easy for readers to understand and routinely used in publications like the online New York Times.
Jun 12 2022
Perspectives On Probability In Operations
The spirited discussions on LinkedIn about whether probabilities are relative frequencies or quantifications of beliefs are guaranteed to baffle practitioners. They come up in threads about manufacturing quality, supply-chain management, and public health, and do not generate much light. Their participants trade barbs without much civility, and without actually exchanging on substance.
The latest one, by Alexander von Felbert, is among the more thoughtful, and therefore unlikely to inspire rants. I do, however, fault it with using words like “aleatory” or “epistemic” that I don’t think are helpful. I am trying to discuss it here in everyday language, and to apply the concepts to numerically specific cases, with an eye to operations.
While there are genuinely great and not-so-great ideas, the root of the most violent disagreements is elsewhere, with individuals generalizing from different experience bases. You may map probability to reality differently depending on whether you are developing drugs in the pharmaceutical industry, enhancing yield in a semiconductor process, or driving down dppms in auto parts. The math doesn’t care as long as you follow its rules, and it doesn’t invalidate other interpretations.
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By Michel Baudin • Data science • 0 • Tags: Bayesian Statistics, data science, Probability, statistics