From the bestselling author of Blink and The Tipping Point, Malcolm Gladwell's Outliers: The Story of Success overturns conventional wisdom about genius to show us what makes an ordinary person an extreme overachiever. Why do some people achieve so much more than others? Can they lie so far out of the ordinary? In this provocative and inspiring book, Malcolm Gladwell looks at everyone from rock stars to professional athletes, software billionaires to scientific geniuses, to show that the story of success is far more surprising, and far more fascinating, than we could ever have imagined. He reveals that it's as much about where we're from and what we do, as who we are - and that no one, not even a genius, ever makes it alone. Outliers will change the way you think about your own life story, and about what makes us all unique. 'Gladwell is not only a brilliant storyteller; he can see what those stories tell us, the lessons they contain' Guardian 'Malcolm Gladwell is a global phenomenon ... he has a genius for making everything he writes seem like an impossible adventure' Observer 'He is the best kind of writer - the kind who makes you feel like you're a genius, rather than he's a genius' The Times
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The problem of outliers is one of the oldest in statistics, and during the last century and a half interest in it has waxed and waned several times. Currently it is once again an active research area after some years of relative neglect, and recent work has solved a number of old problems in outlier theory, and identified new ones. The major results are, however, scattered amongst many journal articles, and for some time there has been a clear need to bring them together in one place. That was the original intention of this monograph: but during execution it became clear that the existing theory of outliers was deficient in several areas, and so the monograph also contains a number of new results and conjectures. In view of the enormous volume ofliterature on the outlier problem and its cousins, no attempt has been made to make the coverage exhaustive. The material is concerned almost entirely with the use of outlier tests that are known (or may reasonably be expected) to be optimal in some way. Such topics as robust estimation are largely ignored, being covered more adequately in other sources. The numerous ad hoc statistics proposed in the early work on the grounds of intuitive appeal or computational simplicity also are not discussed in any detail.
From New York Times bestselling author Kimberley McCreight comes the first book in a breathtakingly brilliant new trilogy, packed with tension, romance and thrilling twists and turns. They’ll get inside your head...
From its initial publication this book has been the standard text on the subject. Since then there has been a continuing high level of activity, and work has developed in all major areas. This third edition reflects the latest state of knowledge with fully revised and extended coverage of all topics. Additional topics and new emphases are presented and a richer coverage of practical fields and computer-based facilities, together with a fully updated reference list, are provided.
Outliers: The Story of Success(2008) by Malcolm Gladwell examines statistically anomalous people and events to better understand why individuals succeed or fail. Too many people toil under the myth of meritocracy… Purchase this in-depth summary to learn more.
Outliers ...in 30 minutes is the essential guide to quickly understanding the ideas explored in Malcolm Gladwell's bestselling book, Outliers: The Story of Success. Understand the key ideas behind Outliers in a fraction of the time: • 27 essential insights and takeaways • 11 illustrative case studies • 9 chapter-by-chapter synopses In Outliers, bestselling author Malcolm Gladwell examines the fundamentals of extraordinary success and the people who achieve it. Claiming that society pays too much attention to characteristics like intelligence and ambition in successful people and not enough to the circumstances that shape them, Gladwell argues that external forces also propel the highly successful. As demonstrated in Outliers, culture, family, demographics, chance circumstances, and hidden advantages can have as much, or more, to do with an outlier's success as IQ or drive. Throughout Outliers, Gladwell uses compelling research and fascinating case studies to demonstrate his assertion that nobody achieves success on his or her own merits. A 30 Minute Expert Summary of Outliers Designed for those whose desire to learn exceeds the time they have available, the Outliers summary helps readers quickly and easily become experts ...in 30 minutes.
The problem of monitoring atmospheric radionuclides over time is investigated. Such monitoring is desirable for both natural and anthropogenic radionuclides. The statistical problem is one of testing for a time series outlier, and the problem is complicated by the fact that often several observations may be missing. In fact it may be the case that several missing observations may occur immediately prior to a data value that is to be tested as an outlier. Evans (1996) proposes an exponentially weighted moving average (EWMA) approach for detecting these outliers. The EWMA approach is one that is quite popular in practice, but it is restricted to some extent by the fact that it is based on the assumption that the autoregressive integrated moving average model, ARIMA(0,1,1), is a good fit to the data. Evans presents simulation results based on simulated radionuclide data obtained from a model that he fit to Kuwait Be7 data consisting of a sinusoidal component with long period plus an autoregressive component. One problem with Evans' approach is that false alarm rates tend to be high when the data value to be tested as an outlier is preceded by a string of missing observations. In this paper we describe several alternative approaches for outlier detection, and we compare these with the Evans method using a simulation study. In this study, outlier detection capabilities are compared in the case in which no data are missing immediately prior to the data value to be tested as an outlier as well as in the more difficult case in which several data values are missing immediately prior to this value. Our results indicate that an autoregressive-based procedure suggested here has much better control over the false alarm rates than does the Evans procedure, and it has detection capability that is comparable to and sometimes better than that obtained by the Evans approach.