Course details

Six Sigma Data Classification, Sampling, and Collection

Six Sigma Data Classification, Sampling, and Collection


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description
This course covers data types, measurement scales, data collection, and sampling techniques. This course is aligned with ASQ's 2015 Six Sigma Green Belt Body of Knowledge.

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

Prerequisites
None

Expected Duration (hours)
1.1

Lesson Objectives

Six Sigma Data Classification, Sampling, and Collection

  • distinguish between data classifications
  • distinguish between types of data
  • determine whether to use discrete or continuous data, given a scenario
  • determine the type of measurement scale being used, given an example
  • identify principles of data sampling
  • distinguish between sampling methods
  • identify situations when you would use simple random sampling
  • identify situations when you would use stratified sampling
  • identify characteristics of automated data collection
  • identify data collection best practices
  • distinguish between technologies used for data collection
  • identify key considerations in creating a data collection plan
  • distinguish between types of check sheets
  • distinguish between data coding methods
  • demonstrate your understanding of data classification, sampling, and collection in Six Sigma improvement initiatives
  • Course Number:
    apr_03_a03_bs_enus

    Expertise Level
    Intermediate