Data Classification and Collection in Six Sigma

Overview/Description
"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.

Target Audience
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

Expected Duration (hours)
1.5

Lesson Objectives

Data Classification and Collection in Six Sigma

distinguish between examples of continuous and discrete data

identify the key characteristics of continuous and discrete data

match the types of measurement scales to their descriptions

match the key considerations for creating a solid data collection plan to their examples

identify an example of a well-created check sheet

identify examples of different types of data coding methods

match the different types of sampling methods used in Six Sigma to their descriptions

identify the considerations for determining sample size

identify the key characteristics of simple random sampling

identify the tasks associated with taking a stratified sample