Clearly demonstrates a wide range of sampling methods now in use by governments, in business, market and operations research, social science, medicine, public health, agriculture, and accounting. Gives proofs of all the theoretical results used in modern sampling practice. New topics in this edition include the approximate methods developed for the problem of attaching standard errors or confidence limits to nonlinear estimates made from the results of surveys with complex plans.
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apply assume average bias biased binomial calculate Cauchy–Schwarz inequality cluster sampling cluster units coefficient of variation comparisons compute confidence limits consider Corollary correlation cost function denotes deviations domain drawn equal probabilities equation estimated variance example farm finite population follows formula frequency distribution given gives Hence households ignoring the fpc interview ith unit known linear regression mean square measured method of sample minimize natural populations negligible normal approximation normally distributed number of units obtained optimum allocation order 1/n population mean population total primary units proportional allocation quantity random number ratio estimate regression estimate relative precision replacement sample mean sampling fraction sampling unit self-weighting shows simple random sample sizes standard error strata stratified random sampling stratified sampling stratum h subsample subunits survey systematic sample Table term total number unbiased estimate unbiased sample estimate variables weights