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Beinhaltet den Namen: DouglasWHubbard

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Classical Greeks not only figured out that the planet is round, but had it measured. Eratosthenes calculated its circumference from a lunch-hour measurement at his library in Alexandria during the summer solstice, knowing only his distance from the Tropic of Cancer. Eratosthenes is a hero of Chicago statistician Doug Hubbard, who trains managers in "calibrated estimates," basically closely observed ballpark figures. Here he describes approaches to making more accurate guesses, including when it's worth spending money to take out some of the guesswork. If you didn't get past introductory statistics in college, this is a useful guide to Monte Carlo simulations, Baysean inversion, crowdsourcing and other analytical concepts. Not only does Hubbard open up the black box of predictive modeling, but he also points to ways we can think about thinking: It's risky to rely on just gut instinct, but maybe we can trust our gut once we measure just how far to trust it.… (mehr)
 
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rynk | 5 weitere Rezensionen | Jul 11, 2021 |
What I enjoyed most about this book was how it revealed multiple ways to attack a measurement problem. Even for the most complex or unclear problems, there are ways to bound and frame the approach sufficient for informing decisions. The book is also heavy, zooming from simple examples to the full span of a college-level statistics course. For the reader interested in making measurement and estimation practical, this book is worthy read and could serve as a ready reference.
 
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jpsnow | 5 weitere Rezensionen | May 18, 2019 |
This book is an expansion of the Author's previous edition - "How to Measure Anything" - applied to cyber security. The drive of this edition is clearly towards quantitative analysis, in contrast to the typical cyber risk analysis which is qualitative. The Author introduces several novel concepts, all based on cost/risk balance, to explore making this science more dependent on placing confidence intervals around expert opinions, making those opinions have a quantitative bearing. It's worth the read if you are exploring this field.… (mehr)
 
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jacobusp | Jun 24, 2017 |
We do not know unless we first measure.
 
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shdawson | 5 weitere Rezensionen | May 1, 2017 |

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