Elements of Survey Sampling
Springer Science & Business Media, May 31, 1996 - Mathematics - 390 pages
Modern statistics consists of methods which help in drawing inferences about the population under consideration. These populations may actually exist, or could be generated by repeatedĚ experimentation. The medium of drawing inferences about the population is the sample, which is a subset of measurements selected from the population. Each measurement in the sample is used for making inferences about the population. The populations and also the methods of sample selection differ from one field of science to the other. Social scientists use surveys tocollectthe sample information, whereas the physical scientists employ the method of experimentation for obtaining this information. This is because in social sciences the factors that cause variation in the measurements on the study variable for the population units can not be controlled, whereas in physical sciences these factors can be controlled, at least to some extent, through proper experimental design. Several excellent books on sampling theory are available in the market. These books discuss the theory of sample surveys in great depth and detail, and are suited to the postgraduate students majoring in statistics. Research workers in the field of sampling methodology can also make use of these books. However, not many suitable books are available, which can be used by the students and researchers in the fields of economics, social sciences, extension education, agriculture, medical sciences, business management, etc. These students and workers usually conduct sample surveys during their research projects.
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approximately auxiliary variable calculated chapter cluster sampling coefficient computed confidence interval cost defined determine drawn error of estimator Estimate the average estimate the proportion estimate the total estimating mean estimating total estimator of mean estimator of population estimator of variance example farmers hectare i-th interested in estimating investigator mean square error mean/total method multistage sampling number of units obtained optimum parameter percent relative efficiency place confidence limits population mean population proportion population total population units possible samples preliminary sample proportional allocation random number random variable ratio estimator regression estimator replacement sample respectively sample mean squares sample size sample survey sample units sampling distribution sampling procedure serial numbers simple random sample Solution SRS with replacement SRS without replacement strata stratified sampling stratum study variable subsample systematic sampling total number Unbiased estimator values Variance of estimator variance V(y villages WOR simple random