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Outliers: The Story of Success is a non-fiction book written by Malcolm Gladwell and published by Little, Brown and Company on November 18, 2008. In Outliers , Gladwell examines the factors that contribute to high levels of success.
In statistics, Dixon's Q test, or simply the Q test, is used for identification and rejection of outliers.This assumes normal distribution and per Robert Dean and Wilfrid Dixon, and others, this test should be used sparingly and never more than once in a data set.
Malcolm Timothy Gladwell CM (born 3 September 1963) is a Canadian journalist, author, and public speaker. [2] He has been a staff writer for The New Yorker since 1996. He has published eight books.
Blink: The Power of Thinking Without Thinking (2005) is Malcolm Gladwell's second book. It presents in popular science format research from psychology and behavioral economics on the adaptive unconscious: mental processes that work rapidly and automatically from relatively little information.
In statistics, Grubbs's test or the Grubbs test (named after Frank E. Grubbs, who published the test in 1950 [1]), also known as the maximum normalized residual test or extreme studentized deviate test, is a test used to detect outliers in a univariate data set assumed to come from a normally distributed population.
The book has seven chapters. [1] [4] The first is introductory; it describes simple linear regression (in which there is only one independent variable), discusses the possibility of outliers that corrupt either the dependent or the independent variable, provides examples in which outliers produce misleading results, defines the breakdown point, and briefly introduces several methods for robust ...
Taleb's "black swan theory" (which differs from the earlier philosophical versions of the problem) refers only to statistically unexpected events of large magnitude and consequence and their dominant role in history. Such events, considered extreme outliers, collectively play vastly larger roles than regular occurrences.
The idea behind Chauvenet's criterion finds a probability band that reasonably contains all n samples of a data set, centred on the mean of a normal distribution.By doing this, any data point from the n samples that lies outside this probability band can be considered an outlier, removed from the data set, and a new mean and standard deviation based on the remaining values and new sample size ...