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# Sampling and Regression Analysis (Statistics Project Sample)

Instructions:

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.

Content:

Module 4 Discussions: sampling and regression analysis

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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).

Examples

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 management information system. If there are 1,000 organizations arranged in an alphabetical order and the sample size is 100, then the first company is chosen randomly and each 10th item is then chosen.

Regression

Regression analysis is necessary to determine the causal relationship among dependent variable and the independent variable (s). One of the purposes of regression is to show how the outcome changes (dependent variable), when one or more of the predictors vary. Additionally, the cause analysis determines the strength of the predictors of the outcome. At other times, regression analysis helps to determine the outcome changes over time in the time series analysis.

Examples

One of the most common applications of regression analysis is demand analysis, which focuses the purchase for products and units (Moon, 2013). For instance, a movie theater might use past information on ticket sales and price to determine the demand for tickets. In this case, the ticket sales are the outcome with the ticket sale price being the predictor.

Regression analysis enhances the decision-making process in a business since decision maker...

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