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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.
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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]
The Q-statistic or q-statistic is a test statistic: The Box-Pierce test outputs a Q-statistic (uppercase) which follows the chi-squared distribution The Ljung-Box test is a modified version of the Box-Pierce test which provides better small sample properties
Dixon's method is based on finding a congruence of squares modulo the integer N which is intended to factor. Fermat's factorization method finds such a congruence by selecting random or pseudo-random x values and hoping that the integer x 2 mod N is a perfect square (in the integers):
An alternative method that determines the similarity among subjects somewhat like Q methodology, as well as the cultural "truth" of the statements used in the test, is Cultural Consensus Theory. The "Q sort" data collection procedure is traditionally done using a paper template and the sample of statements or other stimuli printed on individual ...
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In statistics, the studentized range, denoted q, is the difference between the largest and smallest data in a sample normalized by the sample standard deviation.It is named after William Sealy Gosset (who wrote under the pseudonym "Student"), and was introduced by him in 1927. [1]