The IASSC Lean Six Sigma Study Guide is derived from the IASSC Universally Accepted Lean Six Sigma Body of Knowledge for Black Belts.

**1.0 Define Phase**

### 1.1 The Basics of Six Sigma

1.1.1 Meanings of Six Sigma

1.1.2 General History of Six Sigma & Continuous Improvement

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

1.1.6 Six Sigma Roles & Responsibilities

### 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

a. including DPU, DPMO, FTY, RTY Cycle Time, deriving these metrics .

### 1.3 Selecting Lean Six Sigma Projects

1.3.1 Building a Business Case & Project Charter

1.3.2 Developing Project Metrics

1.3.3 Financial Evaluation & Benefits Capture

### 1.4 The Lean Enterprise

1.4.1 Understanding Lean

1.4.2 The History of Lean

1.4.3 Lean & Six Sigma

1.4.4 The Seven Elements of Waste

a. Overproduction, Correction, Inventory, Motion, Overprocessing, Conveyance, Waiting.

1.4.5 5S

a. Straighten, Shine, Standardize, Self-Discipline, Sort

**2.0 Measure Phase**

### 2.1 Process Definition

2.1.1 Cause & Effect / Fishbone Diagrams

2.1.2 Process Mapping, SIPOC, Value Stream Map

2.1.3 X-Y Diagram

2.1.4 Failure Modes & Effects Analysis (FMEA)

### 2.2 Six Sigma Statistics

2.2.1 Basic Statistics

2.2.2 Descriptive Statistics

2.2.3 Normal Distributions & Normality

2.2.4 Graphical Analysis

### 2.3 Measurement System Analysis

2.3.1 Precision & Accuracy

2.3.2 Bias, Linearity & Stability

2.3.3 Gage Repeatability & Reproducibility

2.3.4 Variable & Attribute MSA

### 2.4 Process Capability

2.4.1 Capability Analysis

2.4.2 Concept of Stability

2.4.3 Attribute & Discrete Capability

2.4.4 Monitoring Techniques

**3.0 Analyze Phase**

### 3.1 Patterns of Variation

3.1.1 Multi-Vari Analysis

3.1.2 Classes of Distributions

3.2 Inferential Statistics

3.2.1 Understanding Inference

3.2.2 Sampling Techniques & Uses

3.2.3 Central Limit Theorem

### 3.3 Hypothesis Testing

3.3.1 General Concepts & Goals of Hypothesis Testing

3.3.2 Significance; Practical vs. Statistical

3.3.3 Risk; Alpha & Beta

3.3.4 Types of Hypothesis Test

### 3.4 Hypothesis Testing with Normal Data

3.4.1 1 & 2 sample t-tests

3.4.2 1 sample variance

3.4.3 One Way ANOVA

a. Including Tests of Equal Variance, Normality Testing and Sample Size calculation, performing tests and interpreting results.

### 3.5 Hypothesis Testing with Non-Normal Data

3.5.1 Mann-Whitney

3.5.2 Kruskal-Wallis

3.5.3 Mood’s Median

3.5.4 Friedman

3.5.5 1 Sample Sign

3.5.6 1 Sample Wilcoxon

3.5.7 One and Two Sample Proportion

3.5.8 Chi-Squared (Contingency Tables)

a. Including Tests of Equal Variance, Normality Testing and Sample Size calculation, performing tests and interpreting results.

**4.0 Improve Phase**

### 4.1 Simple Linear Regression

4.1.1 Correlation

4.1.2 Regression Equations

4.1.3 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 Residuals Analysis

4.2.5 Data Transformation, Box Cox

### 4.3 Designed Experiments

4.3.1 Experiment Objectives

4.3.2 Experimental Methods

4.3.3 Experiment Design Considerations

### 4.4 Full Factorial Experiments

4.4.1 2k Full Factorial Designs

4.4.2 Linear & Quadratic Mathematical Models

4.4.3 Balanced & Orthogonal Designs

4.4.4 Fit, Diagnose Model and Center Points

### 4.5 Fractional Factorial Experiments

4.5.1 Designs

4.5.2 Confounding Effects

4.5.3 Experimental Resolution

**5.0 Control Phase**

### 5.1 Lean Controls

5.1.1 Control Methods for 5S

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 Attribute Charts (U Chart, P Chart, NP Chart)

5.2.7 X-S chart

5.2.8 CuSum Chart

5.2.9 EWMA Chart

5.2.10 Control Methods

5.2.11 Control Chart Anatomy

5.2.12 Subgroups, Impact of Variation, Frequency of Sampling

5.2.13 Center Line & Control Limit Calculations

### 5.3 Six Sigma Control Plans

5.3.1 Cost Benefit Analysis

5.3.2 Elements of the Control Plan

5.3.3 Elements of the Response Plan

**Levels of Cognition based on Bloom’s Taxonomy – Revised (2001)**

These levels are from “Levels of Cognition” (from Bloom’s Taxonomy – Revised, 2001). They are listed in order from the least complex to the most complex.

**Remember**:
Recall or recognize terms, definitions, facts, ideas, materials, patterns, sequences, methods, principles, etc.

**Understand**:
Read and understand descriptions, communications, reports, tables, diagrams, directions, regulations, etc.

**Apply**:
Know when and how to use ideas, procedures, methods, formulas, principles, theories, etc.

**Analyze**:
Break down information into its constituent parts and recognize their relationship to one another and how they are organized; identify sublevel factors or salient data from a complex scenario.

**Evaluate**:
Make judgments about the value of proposed ideas, solutions, etc., by comparing the proposal to specific criteria or standards.

**Create**:
Put parts or elements together in such a way as to reveal a pattern or structure not clearly there before; identify which data or information from a complex set is appropriate to examine further or from which supported conclusions can be drawn.

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