This course will strive to give you everything an up and coming 6 sigma wielding professional needs to pass his/her Green Belt certification exam and NO MORE. The structure of the course is based off the official Green Belt Body of Knowledge for IASSC.

Green Belt Body of Knowledge

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1.0 Define Phase
1.1 The Basics of Six Sigma
1.2 Meanings of Six Sigma
1.1.2 General History of Six Sigma & Continuous Improvement
1.1.2.1 Notable Doctors of Quality
1.1.3 Deliverables of a Lean Six Sigma Project
1.1.4 The Problem Solving Strategy Y = f(x)
1.1.5 Voice of the Customer, Business and Employee
1.1.5.1 Voice of the Customer
1.1.5.2 Voice of the Business
1.1.5.3 Voice of the Employee
1.1.5.4 Voice of the Process
1.1.6 Six Sigma Roles & Responsibilities
1.1.6.1 Yellow Belt
1.1.6.2 Green Belt
1.1.6.3 Black Belt
1.1.6.4 Master Black Belt
1.1.6.5 Champion
1.1.6.6 Change Agent
1.2 The Fundamentals of Six Sigma
1.2.1 Defining a Process
1.2.2 Critical to Quality Characteristics (CTQ’s)
1.2.3 Cost of Poor Quality (COPQ)
1.2.4 Pareto Analysis (80:20 rule)
1.2.5 Basic Six Sigma Metrics
1.2.5.1 Defect per Unit (DPU)
1.2.5.2 Defect-per-Million Opportunities (DPMO)
1.2.5.3 Sigma Level
1.2.5.4 First Time Yield (FTY)
1.2.5.5 Throughput Yield
1.2.5.6 Rolled Throughput Yield (RTY)
1.2.5.7 Cycle Time
1.2.5.8 Lead Time
1.3 Selecting Lean Six Sigma Projects
1.3.1 Building a Business Case & Project Charter
1.3.1.1 Business Case
1.3.1.2 Project Charter
1.3.2 Developing Project Metrics
1.3.3 Financial Evaluation & Benefits Capture
1.3.3.1 Cost Benefit Analysis
1.3.3.2 Break Even Point
1.3.3.3 Net Present Value (NPV)
1.3.3.4 Opportunity Cost
1.4 The Lean Enterprise
1.4.1 Understanding Lean
1.4.2 History of Lean
1.4.3 Lean & Six Sigma
1.4.4 The Seven Elements of Waste
1.4.5 5S
2.0 Measure Phase
2.1 Process Definition
2.1.1 Cause & Effect / Fishbone Diagrams
2.1.2 Process Mapping, SIPOC, Value Stream Map
2.1.2.1 Process Mapping
2.1.2.2 SIPOC
2.1.2.3 Value Stream Map
2.1.3 X-Y Diagram
2.1.4 Failure Modes & Effects Analysis (FMEA)
2.1.4.1 Potential Failure Modes (PFM)
2.1.4.2 Criticality
2.1.4.3 Risk Priority Number (RPN)
2.2 Six Sigma Statistics
2.2.1 Basic Statistics
2.2.1.1 Mean
2.2.1.1.1 Population Mean
2.2.1.1.2 Sample Mean
2.2.1.1.3 Weighted Mean
2.2.1.2 Range
2.2.1.3 Mean Deviation
2.2.1.4 Variance
2.2.1.4.1 Population Variance
2.2.1.4.2 Sample Variance
2.2.1.5 Standard Deviation
2.2.1.5.1 Population Standard Deviation
2.2.1.5.2 Sample Standard Deviation
2.2.2 Descriptive Statistics
2.2.3 Normal Distributions & Normality
2.2.4 Graphical Analysis
2.2.4.1 Frequency Distributions
2.2.4.2 Box-and-Whisker Plot
2.2.4.3 Run Charts
2.2.4.4 X-Y Diagram
2.3 Measurement System Analysis (MSA)
2.3.1 Precision & Accuracy
2.3.1.1 Precision
2.3.1.2 Accuracy
2.3.1.3 True Value
2.3.2 Bias, Linearity & Stability
2.3.2.1 Bias
2.3.2.3 Linearity
2.3.2.3 Stability
2.3.3 Gage Repeatability & Reproducibility
2.3.3.1 Repeatability
2.3.3.2 Reproducibility
2.3.4 Variable & Attribute MSA
2.3.4.1 Variable MSA
2.3.4.2 Attribute MSA
2.4 Process Capability
2.4.1 Capability Analysis
2.4.1.1 Specification Limits
2.4.1.2 CP
2.4.1.3 CPK
2.4.1.4 PP or PPK
2.4.2 Concept of Stability
2.4.3 Attribute & Discrete Capability
2.4.3.1 Attribute Data
2.4.3.2 Discrete Data
2.4.4 Monitoring Techniques
3.0 Analyze Phase
3.1 Patterns of Variation
3.1.0.1 Common Cause Variation
3.1.0.2 Special Cause Variation
3.1.0.3 Cyclical Variation
3.1.0.4 Positional Variation
3.1.0.5 Temporal Variation
3.1.1 Multi-Variable Analysis
3.1.2 Classes of Distributions
3.1.2.1 Binomial Distribution
3.1.2.2 Normal Distribution
3.1.2.2.1 Z-score
3.1.2.2.2 68-95-99.7 Rule
3.1.2.3 Poisson Distribution
3.1.2.4 Chi Square Distribution
3.1.2.5 Student t Distribution
3.1.2.6 F Distribution
3.2 Inferential Statistics
3.2.1 Understanding Inference
3.2.2 Sampling Techniques & Uses
3.2.2.1 Simple Random Sampling
3.2.2.2 Stratified Random Sampling
3.2.2.3 Cluster Sampling
3.2.2.4 Systematic Sampling
3.2.2.5 Convenience (Haphazard) Sampling
3.2.2.6 Volunteer Sampling
3.2.3 Central Limit Theorem
3.3 Hypothesis Testing
3.3.1 Significance; Practical vs. Statistical
3.3.1.1 Practical Significance
3.3.1.2 Statistical Significance
3.3.2 Risk; Alpha & Beta
3.3.2.1 Alpha Risk
3.3.2.2 Beta Risk
3.4 Hypothesis Testing with Normal Data
3.4.1 Student t Tests
3.4.1.1 Unequal Variance 2 Sample t Test
3.4.1.2 Equal (Pooled) Variance t Test
3.4.1.3 Paired t test
3.4.1.4 Single Sample t Test
3.4.2 Z Test
3.4.2.1 1 Sample Z Test
3.4.2.2 2 Sample Z Test
3.4.3 One Way ANOVA
3.5 Hypothesis Testing with Non-Normal Data
3.5.1 Single Sample Test of Proportion
3.5.2 Two Sample Test of Proportion
3.5.3 Chi-Squared (Contingency Tables)
3.5.4 1 Sample Sign Test
3.5.5 1 Sample Wilcoxon Test
3.5.6 Friedman Test
3.5.7 Mann-Whitney Test
3.5.8 Kruskal-Wallis Test
3.5.9 Mood’s Median Test
4.0 Improve Phase
4.1 Simple Linear Regression
4.1.1 Correlation
4.1.2 Residuals Analysis
4.2 Multiple Regression Analysis
4.2.1 Non- Linear Regression
4.2.2 Multiple Linear Regression
4.2.3 Confidence & Prediction Intervals
4.2.4 Data Transformation
4.2.4.1 Box Cox
5.0 Control Phase
5.1 Lean Controls
5.1.1 Control Methods for 5S
5.1.1.2 Visual Factory
5.1.2 Kanban
5.1.3 Poka-Yoke (Mistake Proofing)
5.2 Statistical Process Control (SPC)
5.2.1 Data Collection for SPC
5.2.2 I-MR Chart
5.2.3 Xbar-R Chart
5.2.4 U Chart
5.2.5 C Chart
5.2.5 P Chart
5.2.6 NP Chart
5.2.7 Xbar-S Chart
5.2.8 CuSum Chart
5.2.9 EWMA Chart
5.2.10 Control Chart Anatomy
5.3 Six Sigma Control Plans
5.3.1 Elements of the Control Plan
5.3.2 Elements of the Response Plan

Disclaimer: This is not an accredited course by any accreditation agency. I am not responsible for any failed tests. This is an informal study guide at best. There has definitely been an attempt to cover enough of the Body of Knowledge, but I would still recommend looking at other sources.