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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.
Frank Ephraim Grubbs (September 2, 1913 – January 19, 2000) was an American statistician. Grubbs's test for outliers, and the Mann-Grubbs method for calculating a binomial series lower confidence bound, are named after him. He worked at the Ballistic Research Laboratory while he was a Captain in the U.S. Army.
Grubbs's test for outliers; Dixon's Q test; ASTM E178: Standard Practice for Dealing With Outlying Observations [13] Mahalanobis distance and leverage are often used to detect outliers, especially in the development of linear regression models. Subspace and correlation based techniques for high-dimensional numerical data [12]
Pages in category "Statistical outliers" The following 17 pages are in this category, out of 17 total. ... Grubbs's test; I. Isolation forest; L. Local outlier factor ...
Grubbs's test for outliers; Guess value; Guesstimate; Gumbel distribution; Guttman scale; ... Outlier; Outliers in statistics – redirects to Robust statistics (section)
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This is an important technique in the detection of outliers. It is among several named in honor of William Sealey Gosset, who wrote under the pseudonym "Student" (e.g., Student's distribution). Dividing a statistic by a sample standard deviation is called studentizing, in analogy with standardizing and normalizing.
Pickleball Rocks revenue was projected to top $1 million in 2022, but to meet the goal Grubbs needed capital — and to get it he turned to his well-heeled friends in the sport, pitching a limited ...