Sampling and Regression Analysis (Statistics Project Sample)
In this week's readings, simple random sampling, systematic, stratified, and cluster sampling are discussed. Define each of the sampling methods. Then, post two examples of sampling situations
BUT DO NOT IDENTIFY THE TYPE OF SAMPLING. Identify and discuss the types of sampling represented in your peers’ examples.
Regression is a commonly used technique in business. What is the purpose of regression? Provide two examples of situations in business where regression may be useful. Review your peers’ posts and identify the independent and dependent variables in their examples.
Module 4 Discussions: sampling and regression analysis
Simple random sampling- This is a design method where samples are chosen randomly, and each has an equal chance of being selected (Black, 2012).
Systematic sampling- This is a probability sampling method, where samples are selected from the population, with the first element selected randomly, and the next data items chosen periodically at a fixed interval (Black, 2012).
Stratified sampling- In this sampling method, the population is divided into groups (strata), and the groups share similar characteristics or attributes. Samples are then selected randomly from a stratum, with the selections forming part of the random sample (Black, 2012).
Cluster sampling-In this sampling method the population is also divided into cluster groups, and the clusters are selected randomly. Data analysis is based on the sampled clusters, which have equal sample sizes (Black, 2012).
For instance, in a study survey to understand attitudes towards workplace harassment, where companies are first chosen in the first stage. The next stage is to sample employees working within the companies.
Another example is whereby a researcher wants to know companies that use a specific ...
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