Overview

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 use 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 below1) INTRODUCTION2) Minitab Boot CAMP2.1 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 Programs2.8 Creating a Report within Minitab3) data manipulation3.1 Introduction to Data Manipulation3.2 Split a Worksheet3.3 Using Recode3.4 Subsetting the Worksheet3.5 Extract numeric data from a cell with Date/Time format3.6 Using the calculator3.7 Assigning a Function3.8 Setting the Decimal Places & Rounding Values3.9 Deleting data3.10 Conditional Formatting3.11 Using the Command Line to Execute Historical Commands4) BUILDING GRAPHS4.1 Introduction to Building GraphsPart 1 The Graph Builder4.2 Introduction to the Graph Builder4.3 Histograms with the Graph Builder4.4 Scatterplots with the Graph Builder4.5 Time Series Plot with the Graph Builder4.6 Probability Plot with the Graph BuilderPart 2 Graphical Analysis with the Assistant4.7 Introduction to Graphical Analysis with the Assistant4.8 Main Effects Plot4.9 Main Effects Screener4.10 Scatterplot ScreenerPart 3 Producing Graphs with the Traditional Menu's4.11 Boxplots and the Menu System4.12 Editing Graphs4.13 Duplicating Graphs4.14 The Individual Value plot4.15 The Dot plot4.16 The Bar Chart4.17 Changing the Order on a Categorical Axis4.18 The Time Series Plot4.19 The Bubble Plot4.20 The Pareto Plot4.21 The Scatter plots4.22 The Marginal Plot4.23 The Parallel Co-ordinate PlotPart4 Minitab 20's New Graphs4.24 The Heat Map4.25 The Binned Scatter Plot5) Core Statistics5.1 Introduction.5.2 Types of Data5.3 Measuring the Centre of a Data Sample5.4 Measuring the Variation of a Data Sample5.5 Populations and Samples5.6 Confidence Intervals5.7 The Normal Distribution5.8 The Central Limit Theorem6) An Overview of Hypothesis Testing6.1 What is Hypothesis Testing.6.2 Understanding the Procedure7) Hypothesis Testing7.1 Introduction to Hypothesis Testing.Tests Comparing One Sample to a Target7.2 1 Sample T7.3 1 Sample StDev7.4 1 Sample % Defective7.5 Chi-Square Goodness of FitTests Comparing Two Samples with Each Other7.6 2 Sample T7.7 Paired T7.8 2 Sample StDevTests Capable of Comparing more than Two Samples7.9 Chi-Square % Defective7.10 Chi-Square Test for Association7.11 Hypothesis Testing Exercises8) ANOVA8.1 Introduction to ANOVA8.2 The theory behind ANOVA8.3 One-Way ANOVA8.4 StDev Test using the Assistant8.5 ANOVA General Liner model (GLM)8.6 GLM examples8.7 ANOVA Exercises9) Control charts9.1 Introduction to Control Charts9.2 False Alarms9.3 Subgrouping9.4 Subgroup Size and Sampling Frequency9.5 Detection Rules used for Special Cause Variation9.6 Control Chart Selection for Continuous Data9.7 I-MR Chart9.8 Xbar-R Chart9.9 Xbar-S Chart9.10 Control Charts for Attribute Data9.11 Control Chart Exercise10) Process Capability10.1 Simple Process Yield Metrics10.2 Introduction to Process Capability10.3 Basic Process Capability.10.4 Z Scores10.5 Sigma Shift10.6 Off-Centre Distributions10.7 Overall Capability and Within Capability10.8 Normality10.9 Summary of Process Capability10.10 Process Capability for Continuous Data10.11 Process Capability for Attribute Data10.12 Process Capability Exercises11) Evaluation of Measurement Systems11.1 What is Measurement System Analysis (MSA)11.2 Why MSA fundamental to DMAIC11.3 When to Apply MSA11.4 What we Cover11.5 Types of Measurement Error11.6 Accuracy Errors11.7 Precision Errors11.8 A Breakdown of Measurement Errors11.9 Gage R & R Introduction11.10 Gage R & R Crossed11.11 Attribute Agreement Analysis within the Assistant11.12 MSA Exercises12) Regression12.1 What is Regression?12.2 What are we covering?12.3 Simple Regression12.4 Introduction to Multiple Regression12.5 Multi-Collinearity and Variance Inflation Factors12.6 Multiple Regression using the Assistant12.7 Regression Exercises13) Design of Experiments13.1 What is Design of Experiments13.2 DOE Terminology13.3 The DOE flowchart in the Assistant13.4 DOE Considerations13.5 Sequential DOE Bacterial Growth Example13.6 Sequential DOE Fuel Efficiency Example13.7 Sequential DOE Exercise14) Improvement Methodology14.1 Improvement Methodology Part 114.2 Improvement Methodology Part 2

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