Data Classification and Collection in Six Sigma

"Measure what is measurable, and make measurable what is not so" said Galileo Galilei, the famous Italian physicist, mathematician, astronomer, and philosopher. Measuring the key characteristics in your current processes is a very significant step in any Six Sigma improvement journey. As such, sample data from existing processes needs to be identified, collected, presented, and analyzed. Collecting data that is correct and useful is one of the first steps in the measurement process. Various types of data exist, and they all need appropriate treatment during the collection, presentation, and analysis stages. You also need to be careful when applying sampling techniques to ensure data accuracy and integrity. This course will explore continuous and discrete types of data, and nominal, ordinal, interval, and ratio measurement scales. It will also introduce methods for data collection, such as check sheets and coded data, and deals with the issue of data accuracy and integrity, focusing particularly on sampling techniques such as random sampling and stratified sampling. The course is aligned to the ASQ Certified Six Sigma Green Belt certification exam and is designed to assist learners as part of their exam preparation.

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Candidates seeking Six Sigma Green Belt certification; quality professionals, engineers, production managers, and frontline supervisors; process owners and champions charged with the responsibility of improving quality and processes at the organizational or departmental level

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Course Overview|Continuous and Discrete Data| - distinguish between examples of continuous and discrete data| - identify the key characteristics of continuous and discrete data|Measurement Scales| - match the types of measurement scales to their descriptions|The Data Collection Process| - match the key considerations for creating a solid data collection plan to their examples|Check Sheets and Coded Data| - identify an example of a well-created check sheet| - identify examples of different types of data coding methods|Data Sampling Considerations and Methods| - match the different types of sampling methods used in Six Sigma to their descriptions| - identify the considerations for determining sample size|Simple Random Sampling| - identify the key characteristics of simple random sampling|Stratified Sampling| - identify the tasks associated with taking a stratified sample|

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