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Supercrunchers: How Anything Can Be Predicted [Englisch] [Taschenbuch]

Ian Ayres
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1. Mai 2008
When would a casino stop a gambler from playing his next hand? How could a company use statistical analysis to blackball you from the job you want? Why should you worry when customer services pay attention to your needs? 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 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.

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  • Taschenbuch: 272 Seiten
  • Verlag: John Murray Publishers (1. Mai 2008)
  • Sprache: Englisch
  • ISBN-10: 0719564654
  • ISBN-13: 978-0719564659
  • Größe und/oder Gewicht: 1,8 x 12,9 x 19,8 cm
  • Durchschnittliche Kundenbewertung: 3.3 von 5 Sternen  Alle Rezensionen anzeigen (3 Kundenrezensionen)
  • Amazon Bestseller-Rang: Nr. 162.620 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)

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Groundbreaking ... Not only is it fun to read. It just may change the way you think' Stephen D Levitt co-author of Freakonomics 'Entertaining and enlightening' Financial Times 'Convincing' Economist


When would a casino stop a gambler from playing his next hand? How could a company use statistical analysis to blackball you from the job you want? Why should you worry when customer services pay attention to your needs? 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 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.

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Die hilfreichsten Kundenrezensionen
2 von 2 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen Misleading 25. August 2008
Format:Gebundene Ausgabe
By purchasing this book I was hoping to be exposed to the way quantitative methods are used (as a tool!) in various fields. Alternatively I hoped to be exposed in an intuitive way to some breakthrough methods. However, all throughout the text the author opposes 'intuitivists' vs 'number crunchers' via selected examples.
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.
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4.0 von 5 Sternen I wish I had read this book earlier 10. April 2011
I just finished reading this book in five hours. I haven't put it aside. For other reasons one might would imagine.

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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Von E. Yang
I had to buy this for class. It provides a lot of examples of good uses of data mining/statistical analysis. It doesn't go into extreme detail as it is really an overview of many uses. Because of this, it's a great introduction and eye-opener on super crunching.
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Die hilfreichsten Kundenrezensionen auf (beta) 3.6 von 5 Sternen  128 Rezensionen
283 von 316 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen CRUNCHING on Empty, CRUNCHING Blind (Apologies to Jackson Browne) 11. November 2007
Von Steve Koss - Veröffentlicht auf
Format:Gebundene Ausgabe
Is it a new brand of cereal? Or maybe it's a granola bar, or a chunky peanut butter spread? Then again, could it be the latest infomercial exercise device designed to give you the six pack abs you've always dreamed of but know in your heart of hearts you'll never achieve? Actually, it's a book - the title a product of the very methods the book describes. Here's what SUPER CRUNCHERS says.

(1) Mathematical regression models generated from large datasets often generate better predictions than human experts, and they provide supporting information on the predictive weight and reliability of each explanatory variable.
(2) Well-crafted experiments using randomized trials and control groups provide good market research and behavioral analysis results.
(3) Technological advances - the Internet, massive data storage devices, rapid computation, broadband telecommunication - are making it possible to share more sources of information and create ever-larger databases for analysis.
(4) Today's companies engage in multiple forms of market research by creating and using large databases and large-scale randomized trials.
(5) Many phenomena conform to normal distributions in which 95% of the population will be found within two standard deviations of the mean, the5% balance generally divided evenly in the two tails.

That's it. I just saved you $25.00 U.S. and a half-dozen or more hours learning how a guy from Yale named Ian Ayres collected a bit of information about applied mathematical techniques that have been in practical use for decades, packaged them up, palmed them off as something new, and cooked up the ridiculous name Super Crunching to describe an ostensibly new technological development. Yet "Super Crunching" is nothing more than the author's marketing hype for a couple of standard mathematical methodologies, a creation of nothing from something. There's no new breakthrough here, no new paradigm.

Yes, the anecdotal information about the future prices of wine vintages, Capital One's teaser offerings, and evidence-based medical diagnosis are interesting (hence the two stars rating). The rest, however, is neither prescriptive nor sufficiently critically analytical. Should we go out shopping for a Super Cruncher tomorrow? Should we delight in the increased accuracy of data-driven modeling and prediction, or should we fear the implied manipulation of our desires and the incessant, single-minded drive toward maximum profit at the expense of creativity? Do we really want movies and books to be developed from mathematical models like Epagogix? Do we really want our every keystroke on the Internet to be fodder for market research that manipulates us in response? John Kenneth Galbraith, among others, warned of exogenous, manufactured demand decades ago.

SUPER CRUNCHERS is part business tome, part econometric paean, and part sociology book, but not fully any of the three. No matter how many time the author uses words like "cool" and "humongous" and "amazing," it's still regrettably a "No Sale" even for someone like me who enjoys reading about applied mathematics.
241 von 293 Kunden fanden die folgende Rezension hilfreich
1.0 von 5 Sternen Disappointing 6. Oktober 2007
Von William Addington - Veröffentlicht auf
Format:Gebundene Ausgabe
I read a blurb on this book in the Economist and bought it for that reason. When I read it however, it failed to deliver. It is similar to the Tom Peter's "Search of Excellence" type book with anecdotal stories with little substance. It is overgeneralized and overhypes the models it discusses. The models Ayres discusses are also NOT NEW. I personnally have been creating these types of system for nearly 30 years. What has changed over the years, of course, is greater accessibility of data and a greater capacity to process that data economically. But we still struggle with quality of data issues and appropriateness of model issues -- especially when the models begin to be used by people other than the model creators. The book glosses over this, only providing an example of how Choicepoint used a poor matching algorithm when eliminating felons from Florida's voting roles and even then the author minimizes the problem.

There is no discussion of how these models become abused when implemented as tools where the user of the tool has no knowledge of its limitations, when the model provides suboptimal solutions or what "outliers" are and how to deal with them (although you know immediately when you ARE the outlier and are trapped dealing with a company using a model designed for a population you don't belong to).

This leads us to becoming a nation of people who read off a screen and do what the computer says to do, while turning off our brain. Any wonder you can get outsourced in that scenario? But it must be right -- we Super Crunched it!
52 von 62 Kunden fanden die folgende Rezension hilfreich
1.0 von 5 Sternen Super Disappointing 3. Januar 2008
Von The Professor - Veröffentlicht auf
Format:Gebundene Ausgabe
Like "Freakonomics," this book over-relies on a catchy phrase as a substitute for a thorough exploration of the concepts and issues. The list of concerns includes:
1. Vague definition of the term "supercrunching." Is it "super" because the author wants us to think all statistics are super, or (what I had hoped) is there something about the type of statistics to which he refers that are in fact different from statistics in decision making for the last 40 years? All the talk of large datasets implies that supercrunching is a matter of size, but then why does the very first example of regression involve a model that has only 2 predictors? No need for large data sets for this kind of a model, right? Unless the effect size is tiny, but then, what good is the model? Tell us how things really are new and different now.
2. The book reads like a list of (mostly internet) companies and how fabulous and smart they are for using statistics. Actuarial science has been around for many, many years and again we see little discussion of how the actuarial tradition has become more available outside of the insurance industry. The whole book seems more like a stream of consciousness than an organized conceptual framework about the emergence of statistics as a guide to commercial, medical, and policy making over time.
3. While perhaps an excellent lawyer and professor, the author makes so many misleading or inaccurate remarks about statistics that it could be difficult for someone with a statistics background to enjoy the book. For example, regression is discussed as a technique that is different from the "randomized test," when in fact the randomized test is a design, and the regression (more commonly the "general linear model," including regression, analysis of variance, linear and structural modeling) is the inferential statistical technique used to evaluate the results of the test design. Early in the book, the author talks about how amazing regression is, and then gives and example of how a bank evaluates probability of future actions on the phone based on past behaviors on the phone. This very first example after introducing regression does not involve regression as a prediction technique, but rather actuarial base rates! In fact, I found it very disappointing that actuarial science, probability, and Bayes' theorem (all at least as relevant to data-driven decision-making as the randomized trial) were given so little attention in the book.
4. Finally, the great irony--and part of the "this book is so bad I have to finish it" quality--is that the author writes in an incredibly intuitive manner. The book is full of cognitively biased representation of the topic, owing mainly to "availability" heuristics, for example, the authors' excessive attention to the work of his friends, his roommates, his enemies, his daughter, or the companies he shops from. Better scholarship (or at least more rational) would have involved an initial sampling of all the relevant examples and final selection of the ones that would best illustrate the concepts (which I never really understood--see points 1 and 2). As other reviewers have pointed out, there is also "confirmatory bias" all over the place (presenting only the facts that fit with one's idea)--why aren't the counter arguments and counter-evidence better presented? The author must know that people buying a book on statistics will actually be smart enough to weigh the different sides of an issue. Like I said, I read to the end just to see if there was a "punch line" where the author confesses about his unapologetically intuitive approach to writing--that the book was a joke on the reader.
I would recommend this only to people who know very little about statistics and are unaware how companies like use statistics to improve business. Such readers will be impressed. For everyone else...there are so many better books out there. Paul Meehl would be super-disappointed in this work.
84 von 105 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Outstanding Information and Very Interesting! 30. August 2007
Von Loyd E. Eskildson - Veröffentlicht auf
Format:Gebundene Ausgabe
"Super Crunchers" provides a very readable summary of what can be done to improve performance using the incredible volumes of data accumulated in business, government, health care, and education. Why now? One reason is that the massive amounts of data now available make randomization (essential to valid conclusions) much more achievable than in the past; the other is the low and continually falling costs of computers and storage media.

The bulk of Ayres' work consists of examples (names both companies and the software involved) within each of the sectors previously mentioned. Capital One has been running randomized tests since at least 1995 - tests include page layout, and type and size of offers. Google uses data analysis to fuel its web accelerator (uses your past browsing history to predict pages to be called up next), Wal-Mart's analysis of responses to various employment questions is used to rank potential employees, and Continental Airlines follows up on its own data to design follow-up programs for complaining fliers. Capital One's approach has also been used to evaluate various charity donation-matching programs, and could also be used to evaluate potential billboard and magazine ads. (Similarly, TiVo is now being used to evaluate various TV ads, using the same approach and measuring the relative frequency with which various ads are fast-forwarded through.)

"Offermatica" software not only automates randomization (format, type of offer) for a number of firms, it also analyzes the responses in real time, dramatically cutting the cost of experiments. Thus, no more waiting for hyper-controlled experiments in universities and laboratories that conclude, ALL OTHER THINGS BEING EQUAL (that never happens), eg. red is preferred to blue.

Randomized tests are also increasingly being used to evaluate various government programs, finding eg. that additional job location assistance more than paid for itself for those receiving unemployment benefits, guiding HeadStart programs to target those most likely to benefit.

"Super Crunchers'" health care examples were the most impressive. Don Berwick's "100,000 lives" campaign saved 122,342 lives in an 18 month period through persuading about 3,000 hospitals representing 75% of all beds to focus on six areas of improvement identified through data analyses. These included antiseptic placement of central line catheters in ICUs, elevating heads and washing the mouths of those on respirators, adoption of the latest heart attack treatments, and rapid response teams to patent beds.

Bottom Line: "Super Crunchers" is an exciting vision of what is already possible!
44 von 54 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen The horse versus the locomotive 1. September 2007
Von The Ginger Man - Veröffentlicht auf
Format:Gebundene Ausgabe
Ayres argues that decisions in business and government should be made through the creative utilization of data analysis rather than as the result of anecdotal observation. While this may seem to be almost a truism, Ayres begins by demonstrating how older enterprises like the wine industry and professional baseball both rely more on feeling and experience than on the quantitative method. Both also rejected initial efforts to move towards a more data centric model.

The difference in the two approaches is not just a matter of managerial preference according to the author: "We are in a historic moment of horse vs. locomotive competition where intuitive and experiential expertise is losing out time and time again to number crunching." Examples include hedge fund experts who create value by finding empirical correlations between unrelated factors and the consumer lending business where front line loan officer judgement has been replaced by more reliable centralized formulas.

I have long worked in the telecommunications business in which a surprising number of important decisions such as constructing channel line ups or marketing products is based to a large degree on experience or feeling. As we have moved to a more data-based model, we continue to struggle to achieve the balance Ayres describes as comfort with both numbers and ideas.

Ayres discusses some of the institutional and ideological barriers to such a transition. The shift to Direct Instruction in primary schools, for example, pits "the brute force of numbers" against the professional experience of teachers and the philosophical inclinations of education professionals. In the commercial lending business, super crunching (defined simply enough as "statistical analysis that impacts real-world decisions") has effectively shifted discretion from front line employees to centralized experts, has deflated salaries and has created the potential to export jobs overseas.

Overall, this is a useful discussion of the challenge of blending science and art in management. It brings a wide range of examples into play and achieves balance in its conclusions. Aside from the pure reading experience, I left with some definite plans to explore the use of randomized trials in my business in place of focus groups and simple historical analysis.
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