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To apply a Q test for bad data, arrange the data in order of increasing values and calculate Q as defined: Q = gap range {\displaystyle Q={\frac {\text{gap}}{\text{range}}}} Where gap is the absolute difference between the outlier in question and the closest number to it.
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]
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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 ...
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
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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):
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