In statistics, Cochran's C test, 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 … See more 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 … See more • Bartlett's test • Levene's test • Brown–Forsythe test • Hartley's test • F-test of equality of variances See more The sample variance of data series j is considered an outlier at significance level α if Cj exceeds the upper limit critical value CUL. CUL depends on the desired significance level α, … See more The C test can be generalized to include unbalanced designs, one-sided lower limit tests and two-sided tests at any significance level α, for any number of data series N, and for any number of individual data points nj in data series j. See more • Critical C values • Generalized Variance Outlier Test • Critical F values See more WebSal used a simple shortcut. A z table indicates the proportion of the area of the distribution TO THE LEFT of a given z score. Given that normal distributions are by definition symmetric around their means, if we're looking for the area of just one tail in the positives, we can either subtract the proportion given by the z table from 1, or simply look at the …
Critical Value - Formula, Definition, Examples, Types - Cuemath
WebAug 20, 2007 · There seems to be a possible paradoxical effect at the low dose level. The authors state that Williams's test only found a significant effect at the high dose level. However, on applying a one-tailed test in the direction of trend at the next dose level down (which does not increase the overall type I error), a significant effect is found (P ... WebThe test statistic formula is given as follows: z = p1−p2−0 √p(1−p)( 1 n1+ 1 n2) p 1 − p 2 − 0 p ( 1 − p) ( 1 n 1 + 1 n 2) where p = x1+x2 n1+n2 x 1 + x 2 n 1 + n 2 p1 p 1 is the proportion of sample 1 with sample size n1 n 1 and x1 x 1 number of trials. p2 p 2 is the proportion of sample 2 with sample size n2 n 2 and x2 x 2 number of trials. raynham ma property cards
COCHRAN VARIANCE OUTLIER TEST - NIST
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