Six Sigma using Minitab 21 – for Beginners to Green Belts .

Are you ready to embark on a rewarding career path? Look no further than Six Sigma. Six Sigma has evolved into a powerhouse methodology embraced by industry titans such as Amazon, General Electric, Google, and 3M and many more. Its proven track record in optimizing business processes has led to widespread adoption across diverse sectors including finance, hospitality, and healthcare. The demand for Six Sigma professionals is skyrocketing, making it an excellent career choice.

We recognise that Six Sigma Beginners and Green belts need more support to understand the complex statistical techniques used within Six Sigma, and this has to be delivered effectively. In this course, the author uses his experience of industrial process improvement and Minitab training to provide candidates with the opportunity to really start understanding Six Sigma and get to the Green Belt Level.

Key Features of this course are:

· It covers all main topics used by Six Sigma Green Belts in easy to understand language.

· Improved and updated for Minitab 21.

· The main Six Sigma tools are explained. We usee example-based learning with Quiz Quizzes and exercises to embed the learning.

· The course mainly uses the Assistant and teaches features such as Sequential DOE and Multiple Regression, the Graph Builder.

· Examples cover both continuous and attribute data where possible.

The full contents of the course are given below

1 INTRODUCTION

2 Minitab Boot CAMP

2.1 The Initial layout.

2.2 Working Alongside the Text.

2.3 Opening a File.

2.4 Importing Data from an Excel Spreadsheet.

2.5 Navigating within the Main Window.

2.6 Column Formats.

2.7 Sending Minitab Outputs to Microsoft Office Programs

2.8 Creating a Report within Minitab

3 data manipulation

3.1 Introduction to Data Manipulation

3.2 Split a Worksheet

3.3 Using Recode

3.4 Subsetting the Worksheet

3.5 Extract numeric data from a cell with Date/Time format

3.6 Using the calculator

3.7 Assigning a Function

3.8 Setting the Decimal Places & Rounding Values

3.9 Deleting data

3.10 Conditional Formatting

3.11 Using the Command Line to Execute Historical Commands

4 BUILDING GRAPHS

4.1 Introduction to Building Graphs

Part 1 The Graph Builder

4.2 Introduction to the Graph Builder

4.3 Histograms with the Graph Builder

4.4 Scatterplots with the Graph Builder

4.5 Time Series Plot with the Graph Builder

4.6 Probability Plot with the Graph Builder

Part 2 Graphical Analysis with the Assistant

4.7 Introduction to Graphical Analysis with the Assistant

4.8 Main Effects Plot

4.9 Main Effects Screener

4.10 Scatterplot Screener

Part 3 Producing Graphs with the Traditional Menu’s

4.11 Boxplots and the Menu System

4.12 Editing Graphs

4.13 Duplicating Graphs

4.14 The Individual Value plot

4.15 The Dot plot

4.16 The Bar Chart

4.17 Changing the Order on a Categorical Axis

4.18 The Time Series Plot

4.19 The Bubble Plot

4.20 The Pareto Plot

4.21 The Scatter plots

4.22 The Marginal Plot

4.23 The Parallel Co-ordinate Plot

Part4 Minitab 20’s New Graphs

4.24 The Heat Map

4.25 The Binned Scatter Plot

5 Core Statistics

5.1 Introduction.

5.2 Types of Data

5.3 Measuring the Centre of a Data Sample

5.4 Measuring the Variation of a Data Sample

5.5 Populations and Samples

5.6 Confidence Intervals

5.7 The Normal Distribution

5.8 The Central Limit Theorem

6 An Overview of Hypothesis Testing

6.1 What is Hypothesis Testing.

6.2 Understanding the Procedure

7 Hypothesis Testing

7.1 Introduction to Hypothesis Testing.

Tests Comparing One Sample to a Target

7.2 1 Sample T

7.3 1 Sample StDev

7.4 1 Sample % Defective

7.5 Chi-Square Goodness of Fit

Tests Comparing Two Samples with Each Other

7.6 2 Sample T

7.7 Paired T

7.8 2 Sample StDev

Tests Capable of Comparing more than Two Samples

7.9 Chi-Square % Defective

7.10 Chi-Square Test for Association

7.11 Hypothesis Testing Exercises

8 ANOVA

8.1 Introduction to ANOVA

8.2 The theory behind ANOVA

8.3 One-Way ANOVA

8.4 StDev Test using the Assistant

8.5 ANOVA General Liner model (GLM)

8.6 GLM examples

8.7 ANOVA Exercises

9 Control charts

9.1 Introduction to Control Charts

9.2 False Alarms

9.3 Subgrouping

9.4 Subgroup Size and Sampling Frequency

9.5 Detection Rules used for Special Cause Variation

9.6 Control Chart Selection for Continuous Data

9.7 I-MR Chart

9.8 Xbar-R Chart

9.9 Xbar-S Chart

9.10 Control Charts for Attribute Data

9.11 Control Chart Exercise

10 PRocess Capability

10.1 Simple Process Yield Metrics

10.2 Introduction to Process Capability

10.3 Basic Process Capability.

10.4 Z Scores

10.5 Sigma Shift

10.6 Off-Centre Distributions

10.7 Overall Capability and Within Capability

10.8 Normality

10.9 Summary of Process Capability

10.10 Process Capability for Continuous Data

10.11 Process Capability for Attribute Data

10.12 Process Capability Exercises

11 Evaluation of Measurement Systems

11.1 What is Measurement System Analysis (MSA)

11.2 Why MSA fundamental to DMAIC

11.3 When to Apply MSA

11.4 What we Cover

11.5 Types of Measurement Error

11.6 Accuracy Errors

11.7 Precision Errors

11.8 A Breakdown of Measurement Errors

11.9 Gage R&R Introduction

11.10 Gage R&R Crossed

11.11 Attribute Agreement Analysis within the Assistant

11.12 MSA Exercises

12 Regression

12.1 What is Regression?

12.2 What are we covering?

12.3 Simple Regression

12.4 Introduction to Multiple Regression

12.5 Multi-Collinearity and Variance Inflation Factors

12.6 Multiple Regression using the Assistant

12.7 Regression Exercises

13 Design of Experiments

13.1 What is Design of Experiments

13.2 DOE Terminology

13.3 The DOE flowchart in the Assistant

13.4 DOE Considerations

13.5 Sequential DOE Bacterial Growth Example

13.6 Sequential DOE Fuel Efficiency Example

13.7 Sequential DOE Exercise

14 Improvement Methodology

14.1 Improvement Methodology Part 1

14.2 Improvement Methodology Part 2

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