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Data Classification, Sampling, and Collection in Six Sigma


Overview/Description
Target Audience
Expected Duration
Lesson Objectives
Course Number



Overview/Description
Before a Six Sigma team can begin to improve an organization's processes, it must measure key performance indicators. In doing so, the team identifies, collects, and analyzes data related to the processes. This course introduces basic types of data, such as continuous and discrete data, as well as various measurement scales. You will learn how to plan data collection and how to use data sampling techniques and data collection tools, such as check sheets. This course is aligned to the ASQ Body of Knowledge and is designed to assist Green Belt candidates toward achieving their certification and becoming productive members of their Six Sigma project teams.

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)
1.6

Lesson Objectives

Data Classification, Sampling, and Collection in Six Sigma

  • distinguish between examples of qualitative and quantitative data
  • label examples as either discrete or continuous data
  • determine whether to use discrete or continuous data, given a scenario
  • determine the type of data to gather, given a scenario
  • determine the type of measurement scale being used, given an example
  • demonstrate your understanding about the four levels of measurement
  • identify principles of data sampling
  • match sampling methods with corresponding characteristics
  • identify situations when you would use simple random sampling
  • identify situations when you would use stratified sampling
  • determine the best data sampling method, given a scenario
  • identify characteristics of automated data collection
  • identify data collection best practices
  • recognize characteristics of technologies used for data collection
  • identify key considerations in creating a data collection plan
  • make data collection decisions, given a scenario
  • match types of check sheets with examples of when each type would be used
  • recognize examples of dating coding methods
  • recognize appropriate use of data collection strategies, given a scenario
  • Course Number:
    oper_26_a03_bs_enus