six sigma green belt certification

Six Sigma Green Belt

Course Dates
March 14, 15, 21, 22, 28, 29 & April 4, 5, 11, 12
8:30am-4:30pm (EST) each day

Course Description
Six Sigma Green Belt training is organized around the five main phases of the Six Sigma Process Improvement Roadmap: Define, Measure, Analyze, Improve and Control (DMAIC). Candidates participate in two training sessions, with at least three weeks in between, allowing them to apply the material learned to their project. 

For project-based training, project reviews during each training session maintain project focus. In this case, it is the responsibility of the Champion to ensure that their Belt candidates arrive at class with a proper project charter.

Optional: In between training, QSG can provide coaching and guidance to ensure that the methodology and roadmap are used appropriately. This is a separate service provided apart from this 80-hour training program.

Who Should Attend
This course is designed for process, product, and quality engineers who will lead project teams to achieve quality, yield, and productivity improvements. 

Learning Objectives
Through training, participants will:

  • Know the process improvement roadmap DMAIC to systematically define, measure, analyze, improve, and finally control the process
  • Know how to identify projects and define the project charter
  • Achieve an in-depth understanding of the knowledge based and data based (statistical) methods and tools to characterize and baseline the process
  • Be able to perform Multi-Vari studies to determine the various sources of noise in the process
  • Be able to design experiments to screen, characterize, and optimize the process with respect to controllable process factors 
  • Know about various control methods to ensure that the improvement achieved is sustained 

Course Outline

Week/Phase Objectives Topics
Week 1

(40 hrs)


  • Overview of Six Sigma
  • Document key information about project
  • Describing a process and its characteristics
  • Identify potential inputs (X’s) and outputs (Y’s)
  • Focusing and Prioritizing
  • Collecting data and ensuring accuracy
  • Understand process stability and capability of meeting customer’s requirements
  • Perform Risk Analysis (FMEA) on key input variables
Introduction to Six Sigma


Project Scoping and Chartering

SIPOC/Process Mapping

Cause and Effect Matrix

Introduction to Minitab or JMP

Basic Statistics/Quality Tools

Central Limit Theorem

Introduction to SPC

Measurement System Analysis

Capability Study

Initial Control Plan

Failure Mode and Effects Analysis

Project Review


Week/Phase Objectives Topics
Week 2

(40 hrs)


  • Determine the effect of uncontrollable (noise) and controllable variables on the key output variables through passive data observations.
  • Obtain clues for the Improve phase.
  • Identify key process input variables to be used in the Improve phase.
  • Effectively perform process characterization to understand system behavior including main effects and interactions.
  • Control Methods to ensure process performance is maintained.
Review of Week 1 

Introduction to Multi-Vari Study

Hypothesis Testing

Confidence Intervals

One and Two Sample Comparisons

  • t-tests
  • Assumptions underlying t-tests

Three or more Sample Comparison

  • One Way Analysis of Variance
  • Blocking
  • Residual Analysis

Simple Regression 

Chi Square Test

Non-Parametric Tests for Medians

Sample Size Determination

Introduction to DOE 

General Factorial Designs

Control Methods

Final Control Plan

Project Review



Course Format
80 hours
Instructor-led class training, with opportunities to practice learned skills using prepared data, live demonstrations and data collected real time in class

Minitab or JMP Statistical Software

Course Instructors:
Dave Margil – Consultant, Lean Six Sigma
Paul Mullenix – Consultant, Statistical/Quality Methods and Six Sigma for Manufacturing, Research, and Service Industries

8:30am-4:30pm (EST) each day

Payment Type

Date(s) Attending

Name of Attendee(s)

Name of Company

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