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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.
However, multiple iterations change the probabilities of detection, and the test should not be used for sample sizes of six or fewer since it frequently tags most of the points as outliers. [3] Grubbs's test is defined for the following hypotheses: H 0: There are no outliers in the data set H a: There is exactly one outlier in the data set
From version 10 (SPSS-X) in 1983, data files could contain multiple record types. Prior to SPSS 16.0, different versions of SPSS were available for Windows, Mac OS X and Unix. SPSS Statistics version 13.0 for Mac OS X was not compatible with Intel-based Macintosh computers, due to the Rosetta emulation software causing
Meaning, if a data point is found to be an outlier, it is removed from the data set and the test is applied again with a new average and rejection region. This process is continued until no outliers remain in a data set. Some work has also examined outliers for nominal (or categorical) data.
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 .
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In data analysis, anomaly detection (also referred to as outlier detection and sometimes as novelty detection) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behavior. [1]
Cochran's test, [1] named after William G. Cochran, is a one-sided upper limit variance outlier statistical test .The C test is used to decide if a single estimate of a variance (or a standard deviation) is significantly larger than a group of variances (or standard deviations) with which the single estimate is supposed to be comparable.