- Audio CD
- Verlag: John Murray Publishers Ltd; Auflage: Unabridged edition (20. Januar 2011)
- Sprache: Englisch
- ISBN-10: 1848546017
- ISBN-13: 978-1848546011
- Größe und/oder Gewicht: 12,4 x 2,4 x 14,4 cm
- Durchschnittliche Kundenbewertung: 4 Kundenrezensionen
- Amazon Bestseller-Rang: Nr. 1.375.330 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
Super Crunchers (Englisch)
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"In the past, one could get by on intuition and experience. Times have changed. Today, the name of the game is data. Ian Ayres shows us how and why in this groundbreaking book Super Crunchers. Not only is it fun to read, it just may change the way you think."—Steven D. Levitt, author of Freakonomics
"Data-mining and statistical analysis have suddenly become cool.... Dissecting marketing, politics, and even sports, stuff this complex and important shouldn't be this much fun to read."—Wired
"[Ayres's] thesis is provocative: Complex statistical models could be used to market products more intelligently, craft better movies, and solve health-care problems—if only we could get past our statistics phobia."—Portfolio
"When statistics conflict with expert opinion, bet on statistics....Businesses, consumers, and governments are waking up to the power of analyzing enormous tracts of information."—Discover
"Super Crunchers shows that data-driven decisionmaking is not just revolutionizing baseball and business; it's changing the way that education policy, health care reimbursements, even tax regulations are crafted. Super Crunching is truly reinventing government. Politicians love to tout policy proposals, but they rarely come back and tell you which ones succeeded and which ones failed. Data-driven policy making forces government to ask the bottom line question of 'What works.' That's an approach we can all support."—John Podesta, President of the Center for American Progress
"A lively and yet rigorously careful account of the use of quantitative methods for analysis and decision-making.... Both social scientists and businessmen can profit from this book, while enjoying themselves in the process."—Dr. Kenneth Arrow, Nobel Prize winning economist, and Professor Emeritus at Stanford University
“Ayres’ point is that human beings put far too much faith in their intuition and would often be better off listening to the numbers.... The best stories in the book are about Ayres and other economists he knows, whether they are studying wine, the Supreme Court or jobless benefits.... Ayres himself is one of the [statistical] detectives. He has done fascinating research.”—The New York Times Book Review
"Ian Ayres [is] a law-and-economics guru."—Chronicle of Higher Education
“Lively and enjoyable.... Ayres skillfully demonstrates the importance that statistical literacy can play in our lives, especially now that technology permits it to occur on a scale never before imagined.... Edifying and entertaining."—Publishers Weekly
"Super Crunchers presents a convincing and disturbing vision of a future in which everyday decision-making is increasingly automated, and the role of human judgment restricted to providing input to formulae."—The Economist
"Insightful and delightful!" —Forbes
-- Dieser Text bezieht sich auf eine vergriffene oder nicht verfügbare Ausgabe dieses Titels.
Companies used to rely on human experts and their years of experience to guide them. Now, cutting-edge organizations are mining the data and crunching numbers instead, to come up with more accurate, less biased predictions. As Freakonomics detailed, statistical analysis can reveal the secret levers of causation. But economist Ian Ayres argues that that's only part of the story: super crunching is revolutionizing the way we all make decisions. Beginning with examples of the mathematician who out-predicted wine buffs in determining the best vintages, and the sports scouts who now use statistics rather than intuition to pick winners, Super Crunchers exposes the world of data-miners, introducing the people and the techniques. It illuminates the hidden patterns all around us. No businessperson, academic, student, or consumer (statistically that's everyone) should make another move without getting to grips with thinking-by-numbers -- the new way to be smart, savvy and statistically superior. -- Dieser Text bezieht sich auf eine vergriffene oder nicht verfügbare Ausgabe dieses Titels.Alle Produktbeschreibungen
I find this opposition misleading because as far as most of the models from this book are concerned, 'garbage in ' garbage out' applies. That is to say that these models are made to test hypotheses, therefore it is not exact to oppose intuition to quantitative methods. Thus it is even more inexact to make the point that number crunching is superior to intuition.
Another weak point of the book is that as introductory as it might be only 6 pages out of 220 pages discuss Bayesian methods and they are to be found at the very end of the book.
However, this book provides an excellent discussion on evidence based medicine. Another very interesting part is the one where the authors points out the factors that facilitate number crunching.
In a nutshell, if you know what 'significantly different from zero' and 'everything else being equal' mean, you should be able to find a better use of your time.
Of course, for somebody who has already received training in statistical methods, there is nothing in this book from a scientific and educational point of view. And for those who have a phobia of maths: Don't worry, there is not a single equation to find.
But that somebody would be me. Still, I couldn't put it aside. And I just wish I had read this book earlier. Because if I had, statistics would have become a serious endeavor of mine. If there is a book out there putting in plain text why statistics are important not only to those who try to do serious academic research, it is definitely this one.
Why did I subtract a star? At some point this book becomes kind of redundant. For those willing to skip pages filled with information they already digested not a problem.
But to sum it up: Fun to read, especially as a primer for statistics classes. Nothing that helps you through those classes except for lots of motivation. And you might suddenly understand why _this_ review is showed to you, not any other.
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Super Crunching is crucially about the impact of statistical analysis on real-world decisions. Two core techniques for Super Crunching are the regression and randomization.
1. Regression will make your predictions more accurate (Historical approach):
It all starts with the use of regressions, and although this method is a basic statistical test of causal relationship it's still a very powerful tool that I need to re-introduce in my analytical life.
Regressions make predictions and tell you how precise the prediction is. It tries to hone in on the causal impact of a variable on a dependent. It can tell us the weights to place upon various factors and simultaneously tell us how precisely it was able to estimate these weights.
2. Randomization and large sample sizes (Present/Real-Time approach):
Reliance on historical data increases the difficulty in discerning causation. Large randomized tests work because the distribution amongst the sample are increasingly identical. Think A/B testing on steroids that allows you to quickly test different combinations! Boils down to the averages of the "treated and untreated" groups.
Government has embraced randomization as the best way to test what works. Statistical profiling led to smarter targeting of government support
With finite amounts of data, we can only estimate a finite number of causal effects
3. Neural network
Unlike the regression approach, which estimates the weights to apply to a single equation, the neural approach uses a system of equations represented by a series of interconnected switches.
Computers use historical data to train the equation switches to come up with optimal weights. But while the neural technique can yield powerful predictions, it does a poorer job of telling you why it is working or how much confidence it has in its prediction.
Super Crunching requires analysis of the results of repeated decisions. If you can't measure what you're trying to maximize, you're not going to be able to rely on data-driven decisions.
We humans just overestimate our ability to make good decisions and we're skeptical that a formula that necessarily ignores innumerable pieces of information could do a better job than we could.
The author makes some logical missteps though. when referring to Sherlock Holme's method as deduction (instead of a combination of induction and abduction), and assuming a normal distribution in asset markets (which has been shown to be a poor model for asset prices). It's a bit nitpicky to be sure, which one might let pass for a book on a different topic, but very strange mistakes to make for a book on data science.
Great, quick read otherwise!
Between this book and the Numerati, I feel really smart and also super sketched out by everyone knowing what I buy.
Probably should stop leaving reviews.
Decision makers like physicians are very soon to be replaceable with artificial intelligence. Good or bad? Good if you are the patient because of better decision making. Bad if you are the physician. You can be replaced by techs in the near future. Movie producers...your go/no go "greenlight" decisions can better be made by a data mining analysis approach. Authors? Better watch out...the publisher may soon make decisions about your books based on their digital rating as potential best sellers. Almost any job requiring expert decision making is subject to increasing data analysis that is better than the experts can deliver. Scary that even creative professions are vulnerable. Customers probably will benefit from the application of the "super crunching" approach, but something may be lost (such as professional decision making jobs?). And this trend seems to be exponential as data storage becomes commoditized and processor speeds increase. Anything that can be stored as data can be "mashed up" and mined for mathematical predictive relationships. Since most of us are unaware of the acceleration of this approach, we really can't see the implications until they are on us. Where goeth free will if everything is digitized, predicted, and manipulated?
This book is not for mathematicians. It is for the lay reader. Marketers and business people who aren't aware of these trends will be excited by their potential for use in their own industries. The writing is clear but unexceptional. But the book is incredibly thought provoking if you haven't been aware of the trends. The author, who is a data miner himself, is enthusiastic about the "New Way to Be Smart" and the potentials for vastly improved decision making that can enhance our lives, but we non-crunchers need to be aware of potential freedoms unwittingly given away.
You won't find anything ground breaking stuff here, but I can assure you that you will find the nuts and bolts of analysis, backed up with stories from the real life.
It is an easy read who also gives you a few pointers as to what other literature to read. I can highly recommend it.
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