Six Sigma Data Classification, Sampling, and Collection

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

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Overview/Description
One way of ensuring a successful business process improvement project is to measure key performance indicators (KPIs). And central to evaluating these KPIs is the identification, collection, and analysis of data related to process design and process control. This course covers the different data types relevant to Lean Six Sigma projects. It also explores data classification, as well as methods for sampling and collecting data as part of a Lean process improvement initiative. 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