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Cochran, William G. (1977). Sampling Techniques (Third ed.). Wiley. ISBN 0-471-16240-X. Statistical Methods Applied to Experiments in Agriculture and Biology by George W. Snedecor (Cochran contributed from the fifth (1956) edition) ISBN 0-8138-1561-4; Planning and Analysis of Observational Studies (edited by Lincoln E. Moses and Frederick ...
The first of these sampling schemes is a double use of a sampling method introduced by Lahiri in 1951. [14] The algorithm here is based upon the description by Lohr. [13] Choose a number M = max( x 1, ..., x N) where N is the population size. Choose i at random from a uniform distribution on [1,N]. Choose k at random from a uniform distribution ...
Some of the more basic methods include simple random sampling (SRS, with or without replacement) and systematic sampling for getting a fixed sample size. There is also Bernoulli sampling with a random sample size. More advanced techniques such as stratified sampling and cluster sampling can also be designed to be EPSEM. For example, in cluster ...
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.
Statistical Methods. Author: George W. Snedecor Publication data: 1937, Collegiate Press Description: One of the first comprehensive texts on statistical methods. Reissued as Statistical Methods Applied to Experiments in Agriculture and Biology in 1940 and then again as Statistical Methods with Cochran, WG in 1967. A classic text.
In statistics, the Horvitz–Thompson estimator, named after Daniel G. Horvitz and Donovan J. Thompson, [1] is a method for estimating the total [2] and mean of a pseudo-population in a stratified sample by applying inverse probability weighting to account for the difference in the sampling distribution between the collected data and the target population.
In statistics, the Cochran–Mantel–Haenszel test (CMH) is a test used in the analysis of stratified or matched categorical data. It allows an investigator to test the association between a binary predictor or treatment and a binary outcome such as case or control status while taking into account the stratification. [ 1 ]
The Cochran–Armitage test for trend, [1] [2] named for William Cochran and Peter Armitage, is used in categorical data analysis when the aim is to assess for the presence of an association between a variable with two categories and an ordinal variable with k categories.