Is a 0.92 correlation good? Why, it is great! I think I may be onto something on predicting the Environmental Effects department's annual hours. I have a data category called
Engineering Change Order (ECO) that seems to predict the actual hours about 3 1/2 years in the future. The ECO are simply the new projects started. Evidently it must take around 3 1/2 years to have the project ready for testing. The data is not 1-to-1, meaning the ECO numbers do not equal the EEE hours, but the data generally goes in the same direction.
I also have a prediction tool in place for the Printed Circuit Boards (PCB) cycle time and touch times. My predictors are not excellent at being exactly correct, but generally, the tool predicts correctly more often than the current tool (about 35% to 10%), but since an large overestimate is considered not as serious as an large underestimate, I got the number of catastrophic failures down to 4 to 10%. That means I am overestimating around 55 to 60% of the time. This is not hitting the final goal, so more work ahead for Ryan and me.
Ryan is currently cleaning the historical engineering and designer data so the entries by the same person are actually the same. We want to estimate the experience of each one and the only way to do this is to clean-up the data. He has worked diligently on this. The data is 50000+ lines long. I think the data will be great to have and see if experience correlates with the Schnick Rating I referred to yesterday. we are speculating that it will.
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