Sampling theory of surveys: with applications
Basic theory: simple random sampling; Sampling with varying probabilities; Stratified sampling; Ratio method of estimation; Regression method of estimation; Choice of sampling unit; Sub-sampling; Systematic sampling; Non-sampling errors.
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SIMPLE RANDOM SAMPLING
SAMPLING WITH VARYING PROBABILITIES
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agricultural approximation arithmetic mean assume average bias biased census cent characteristic under study cluster sampling CM CM coefficient consider cost crop denote double sampling draw efficient enumerators equal probability equation esti example expected value expression farm field follows Hence i-th stratum intra-class correlation j-th mean square error Neyman allocation number of second-stage number of units optimum allocation optimum value population mean population value precision primary units probabilities of selection probability proportional procedure proportional allocation random numbers ratio estimate relative sample mean sample survey sampling units sampling variance sampling with varying second-stage units seen simple arithmetic mean simple random sampling standard error Stat strata stratified sampling sub-sampling substituting Sukhatme survey numbers systematic sampling t-th Table term tion total number two-stage sampling unbiased estimate unit of sampling units are selected variance given varying probabilities zero