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Stratified Sampling
Stratified Sampling is generally used when the population is heterogeneous. In this case, the population is first subdivided into several parts (or small groups) called strata. According to some relevant characteristics so that each stratum is more or less homogeneous. Each stratum is called a sub-population. Then a small sample (called sub-sample) is selected from each stratum at random. All the sub-samples combined together form the Stratified Sample. This represents the population properly. The process of obtaining and examining a Stratified Sample with a view to estimate the characteristic of the population is known as Stratified Sampling.
Example
The retailer can use Stratified Sampling as explained below:
She would analyze the bill copies according to items purchased, viz. TVs, Stereos, VCRs, etc. Each product’s customers would form a strata. Hence, we would have the following strata:
TV buyers
Stereo buyers
VCR buyers
For each stratum, random sampling would be done.
Systematic Sampling In Systematic Sampling each element has an equal chance of being selected, but each sample does not have the same chance of being selected. Here,
(a) Elevation (m) 0 400 800 1200 1600 2000 2400 2800 3200 4000 480
Cluster Sampling Here the population is divided into clusters or groups and then Random Sampling is done for each cluster. Cluster Sampling differs from Stratified Sampl
what the purpose we use it
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Descriptive Statistics : Carrying out an extensive analysis the data was not a subject to ambiguity and there were no missing values. Below are descriptive statistics that hav
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