Who Wants to Live Forever? Reliability Engineering and Mortality

Abstract: 

Reliability engineering is the study of survival and failure in engineered systems, but its methods can be applied as well in natural and social sciences, and business. It reveals surprising patterns in the world, including many examples where used is better than new -- that is, we expect a used part to last longer than a new one.

In this talk, I'll present tools of reliability engineering including survival curves, hazard functions, and expected remaining lifetimes. And we'll consider examples from a variety of domains, including light bulbs, computer systems, and life expectancy for humans and institutions.

Intuitively, we expect things to wear out over time: a new car is expected to last longer than a used one, and a young person is expected to live longer than an old person. But many natural and engineered systems defy this intuition. For example, in the last weeks of pregnancy, the process becomes almost memoryless: the expected remaining duration levels off at four days, and stays there for almost four weeks.

Other examples entirely invert our expectations, so the longer something has survived, the longer we expect it to survive. Until recently, nearly every baby born had this property, due to high rates of infant mortality. Computer programs, data transfers, and freight trains have it, too. Understanding this behavior is important for designing computer systems, interpreting a medical prognosis, and maybe finding the key to immortality.

Session Outline:

Introduction to the concepts of survival analysis and tools for working with survival curves and hazard curves in Python.

Background Knowledge:

Basic statistics only

Bio: 

Allen Downey is a curriculum designer at Brilliant.org and professor emeritus at Olin College. He is the author of several books -- including Think Python, Think Bayes, and Probably Overthinking It -- and a blog about data science and Bayesian statistics. He received a Ph.D. in computer science from the University of California, Berkeley; and Bachelor's and Masters degrees from MIT.

Open Data Science

 

 

 

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