The member will learn to define and apply various sampling methods in data collection. Learn how to use four sampling strategies to ensure data is random and representative—simple random sampling, stratified random sampling, systematic sampling, and subgroup sampling.
- If you were asked to measure and determine…the average height of the population, what would you do?…Measure the height of everyone in the country?…No, it's not feasible.…Similarly, when collecting data for the measure phase,…it may not always be possible to measure…every member of the population…or every unit in the process.…That's why sampling is needed.…Sampling is the selection of a small number of items…that is representative of a larger population…with respect to the characteristics you want to measure.…
Such as measuring the height of a small group,…instead of everybody.…To do sampling right, it must also be random.…This means that every item in the population,…or every unit in the process has an equal…chance or probability of being selected.…If your sampling is random and representative…you will avoid bias.…Now, there are several sampling strategies.…But before I share good strategies,…I want to tell you about two that you should avoid.…
The first strategy to avoid is judgment sampling.…This is when you are told to use your judgment…
Dr. Richard Chua builds upon his Six Sigma Foundations and Learning Minitab courses, and covers an array of topics, including measurement system analysis, descriptive statistics, hypothesis testing, design of experiments, statistical process control, and more.
Lynda.com is a PMI Registered Education Provider. This course qualifies for professional development units (PDUs). To view the activity and PDU details for this course, click here.
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- Six Sigma and the organization
- Collecting the voice of the customer
- Project management basics
- Process maps
- Sampling in data collection
- Measurement system analysis
- Measuring performance using descriptive statistics
- Process performance measures
- Hypothesis testing
- Testing for means, variances, proportions, and independence
- Correlation and regression
- Using selection matrices
- Using failure modes and effects analysis
- Developing control plans
- Statistical process control