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In my college statistics class, we learned MatLab and R using data from the Challenger to recreate why the engineers thought the o-ring might fail and raised th
by simple10 2y ago
In my college statistics class, we learned MatLab and R using data from the Challenger to recreate why the engineers thought the o-ring might fail and raised the warning. I don't remember the specifics of all the stats, but it was fascinating and the data is publicly available.
Here's a blog that shows some of the analysis (from google search): https://byuistats.github.io/Statistics-Notebook/Analyses/Logistic%20Regression/Examples/challengerLogisticReg.html https://byuistats.github.io/Statistics-Notebook/Analyses/Log...
- bumby 2y agoI wonder what kind of effect that analysis would have on the launch decision. Here's how I would expect it to go: Engineer: The logistic regression model shows there is a 99.96% chance of failure at 31deg Manager: How good is the model? Engineer: p=0.1157 Manager: So it's not statistically significant by our standards. In other words, it's not a very good model. Engineer: Yeah, but still. Manager: Well, what does the model say the temperature needs to be before we're more likely to have a successful launch than not? Engineer: 64.8 degrees. Manager: I can't defend pushing the schedule to the right that far based on a statistically insignificant model. (It doesn't mean the manager was right, but I think it highlights hindsight bias. The model only seems good in hindsight because it aligns with the outcome we witnessed and not because it's a really good model. The managers at the time didn't have the benefit of hindsight.)