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In statistics, Dixon's Q test, or simply the Q test, ... If Q > Q table, where Q table is a reference value corresponding to the sample size and confidence level, ...
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):
The table shown on the right can be used in a two-sample t-test to estimate the sample sizes of an experimental group and a control group that are of equal size, that is, the total number of individuals in the trial is twice that of the number given, and the desired significance level is 0.05. [4] The parameters used are:
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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The q-value can be interpreted as the false discovery rate (FDR): the proportion of false positives among all positive results. Given a set of test statistics and their associated q-values, rejecting the null hypothesis for all tests whose q-value is less than or equal to some threshold ensures that the expected value of the false discovery rate is .
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 ...