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designs is stratified random sampling. A stratified random sample is one obtained by dividing the population elements into mutually exclusive, non-overlapping groups of sample units called strata, then selecting a simple random sample from within each stratum (stratum is singular for strata). Every potential sample unit must

When proportion allocation is used, the variance of the satisfied random sample estimator is k V (y prop) = 1 – n N h S h 2 h=1 N N h (c) Optimum allocation : Theorem 5 : In stratified random sampling, the variance of Yst is minimum, for a fixed total size of the same, if the sample is allocation with n

3 STRATIFIED SIMPLE RANDOM SAMPLING Suppose the population is partitioned into disjoint sets of sampling units called strata. If a sample is selected within each stratum, then this sampling procedure is known as strati ed sampling. If we can assume the strata are sampled independently across strata, then (i) the estimator of tor y

Stratified Random Sampling SAMPLING UNIT 3 STRATIFIED RANDOM Structure 3.1 Introduction Objectives 3.2 Principles of Stratification Notations and Terminology 3.3 Properties of Stratified Random Sampling 3.4 Mean and Variance for Proportions 3.5 Allocation of Sample Size ... i respectively, various formula for

Stratified Random Sampling The stratified sample. Stratified and Cluster Sampling Robb T. Koether Introduction Stratified Random Samples Example Estimating Parameters Cluster Samples Example ... Definition (Cluster random sampling) Cluster random samplingis a sampling method in which the population is first divided into clusters. Then a simple

Stratified Random Sampling Introduction In stratified random sampling, samples are drawn from a population that has been partitioned into subpopulations (or strata) based on shared characteristics (e.g., gender, age, location, etc.). Each individual stratum is sampled independently of all other strata. The Stratified Random Sampling tool in ...

Stratified Random Sampling To estimate a population average: Sitatwcaffon vanabl]e should be related the variabfle(s) of finite resit- Strata Strata. 1. are a partition of the population Non-overlapping Constitute the whole population 2. Must be defined (partitioned) before sampling .

Suppose that simple random sampling is used to survey each stratum independently. Let n l be the sample size and X l be the sample mean for the lth stratum, l= 1; :::; L. The sample total n= n 1 + :::+ n L is xed. X 1; :::; X L are independent random variables. To simplify our analysis let us not take into account population corrections. We ...

Example: SRS vs. Stratified Sampling Consider a population with 1000 males and 100 females. If we take a Simple Random Sample (SRS) of size 55, it is possible to end up with a sample containing no females. Such a sample might not be representative of the population, especially if males and females respond differently to the item of interest.

Stratified Random Sampling 3.1 Introduction In case of simple random sampling without replacement (SRSWOR), the sampling variance of the sample mean is V(y¯ n) = 1 n − 1 N S2 y. Clearly, the variance decreases as the sample size (n) increases or the population variability S2 y decreases. However, a good sampling strategy is one which helps in

6.3 deals with the problem of selecting a random sample from the population which is given in the stratified form. The process of selecting the random sample of required size in such cases is described with its steps. Section 6.4 is devoted to the usual problem of estimating parameters ‘Population Total’

Stratified Random Sample Definition (Homogeneous) A group ishomogeneousif its member all have similar characteristics with regard to a variable of interest. Definition (Stratum) Astratumis a homogeneous subset of the population. Definition (Stratified random sampling) Stratified random samplingis a sampling method in which

sample is time-saving. The principal properties of the estimate y 11 are outlined in the following theorems. The first two theorems apply to stratified sampling in general and are not restricted to stratified random sampling; that is, the sample from any stratum need not be a simple random sample. 11aeorem S.l.

sampling techniques. To compare sampling method B to A, calculate RE as 2 2 A B x x S RE S = (Note: “Standard” method goes in numerator, i.e., numerator is the method you wish to compare another TO) To compare Stratified Sampling to Simple Random, calculate RE= S x srs 2 S x st 2= 7.436 1.309 =5.68, i.e., stratified sampling is ~ 6 times ...

Proportional Stratified Sample: The number of sampling units drawn from each stratum is in proportion to the relative population size of that stratum Disproportional Stratified Sample: The number of sampling units drawn from each stratum is allocated according to analytical considerations e.g. as variability increases sample

Stratified Random Sample of size n and the technique of drawing such a I. In stratified random sampling the two points, viz., emarks. (i) proper classification of the population into various Strata, and (ii) a suitable sample size from each stratum, are equally important. If the stratification is faulty, it cannot be com.

Stratified Sampling 2012 - Free download as PDF File (.pdf), Text File (.txt) or read online for free. The document discusses stratified sampling, which partitions a population into strata and samples independently within each stratum. It provides formulas to estimate population totals and means from stratified samples, including with stratified random sampling.

Definition (Stratified random sampling) Stratified random samplingis a sampling method in which the population is first divided into strata. Then a simple random sample is taken from each stratum. The combined results constitute the sample. Robb T. Koether (Hampden-Sydney College) Stratified and Cluster Sampling Tue, Jan 26, 2010 7 / 23

In case of stratified simple random sampling, since the samples from different strata are selected independently, each stratum can, therefore, be treated as a separate population. All the results given in chapter 3 can thus be applied to each stratum. The stratified mean estimator will be more efficient than the usual simple random ...