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In statistics, Cochran's C test,〔W.G. Cochran, The distribution of the largest of a set of estimated variances as a fraction of their total, Annals of Human Genetics (London) 11(1), 47–52 (January 1941).〕 named after William G. Cochran, is a one-sided upper limit variance outlier 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. The C test is discussed in many text books 〔D.L. Massart, B.G.M. Vandeginste, L.M.C. Buydens, S. de Jong, P.J. Lewi, J. Smeyers-Verbeke, ''Handbook of Chemometrics and Qualimetrics'': Part A, Elsevier, Amsterdam, The Netherlands, 1997 ISBN 0-444-89724-0.〕〔P. Konieczka, J. Namieśnik, Quality Assurance and Quality Control in the Analytical Chemical Laboratory – A Practical Approach, CRC Press, Boca Raton, Florida, 2009; ISBN 978-1-4200-8270-8.〕〔J.K. Taylor, Quality Assurance of Chemical Measurements, 4th printing, Lewis Publishers, Chelsea, Michigan, 1988; ISBN 0-87371-097-5.〕 and has been recommended by IUPAC 〔W. Horwitz, Harmonized protocol for the design and interpretation of collaborative studies, Trends in Analytical Chemistry 7(4), 118–120 (April 1988).〕 and ISO.〔ISO Standard 5725–2:1994, “Accuracy (trueness and precision) of measurement methods and results – Part 2: Basic method for the determination of repeatability and reproducibility of a standard measurement method”, International Organization for Standardization, Geneva, Switzerland, 1994; http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=11834〕 Cochran's C test should not be confused with Cochran's Q test, which applies to the analysis of two-way randomized block designs. The C test assumes a balanced design, i.e. the considered full data set should consist of individual data series that all have equal size. The C test further assumes that each individual data series is normally distributed. Although primarily an outlier test, the C test is also in use as a simple alternative for regular homoscedasticity tests such as Bartlett's test, Levene's test and the Brown–Forsythe test to check a statistical data set for homogeneity of variances. An even simpler way to check homoscedasticity is provided by Hartley's Fmax test,〔 but Hartley's Fmax test has the disadvantage that it only accounts for the minimum and the maximum of the variance range, while the C test accounts for all variances within the range. ==Description== The C test detects one exceptionally large variance value at a time. The corresponding data series is then omitted from the full data set. According to ISO standard 5725 〔 the C test may be iterated until no further exceptionally large variance values are detected, but such practice may lead to excessive rejections if the underlying data series are not normally distributed. The C test evaluates the ratio: :: where: :''Cj'' 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Cochran's C test」の詳細全文を読む スポンサード リンク
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