These days, my memory isn’t what it used to be. Besides that, my memory isn’t what it used to be. But my incurable case of CRS (can’t remember stuff) is not nearly as bad as that of the exponential distribution.
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When modeling failure data for reliability analysis, the exponential distribution is completely memoryless. It retains no record of the previous failure of an item. That might sound like a bad thing. But this special characteristic makes the distribution extremely useful for modeling the behavior of items that have a constant failure rate.
Using the exponential distribution to model failure data
Suppose you track the time until failure of a randomly collected sample of items. When you graph the results on a histogram, you get something like this:
Notice that the number of items that fail at a given point in time decreases steadily as time elapses. This steady decrease can be fit nicely to an exponential curve.
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Comments
Exponential Example
For those who struggle with the idea of a memoryless property, time between work injuries often follow this distribution. So you sit at the morning meeting and say, "OK, what is the likelihood of having an injury today?". Let's say it is 5%. Then you go without injury today and come in tomorrow and say "OK, what is the likelihood of having an injury today?". It doesn't increase because you are now pressing your luck. It is still 5%. And you go a week without injuries and then come in the next and ask the question. If the environment and causes haven't changed, it is still 5%.
Thanks for the great example
And, by the same token, if you do have a work injury, or injuries, the likelihood is still 5% according to the memoryless property. It doesn't decrease because you "got your injury out of the way". Great concrete example--thanks for commenting.
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