# Book Review: Six Sigma Statistics with Excel and Minitab, ISBN-13: 978-0071489690

Published by McGraw-Hill Companies, Inc. and authored by Issa Bass, this book focuses strictly on Six Sigma Statistics in conjunction with Excel and/or Minitab.  As a general room, the Management and Strategy Institute always recommends using Excel for the majority of project tasks.  We’re glad to see a book like this shine a light on Excel and the many functions it can handle when implementing a project.  Of course, if the project is large enough or complicated enough, software like Minitab can be a real project lifeline and ensure the project stays on target.  The book was first published in 2007, so some of the Minitab information may be outdated as of 2021.  The general information is still applicable, however, so let’s dig in. The chapters break down like this:

• Chapter 1. Introduction, Six Sigma Methodology
• Chapter 2. An Overview of Minitab and Microsoft Excel
• Chapter 3. Basic Tools for Data Collection, Organization and Description
• Chapter 4. Introduction to Basic Probability
• Chapter 5. How to Determine, Analyze, and Interpret Your Samples
• Chapter 6. Hypothesis Testing
• Chapter 7. Statistical Process Control
• Chapter 8. Process Capability Analysis
• Chapter 9. Analysis of Variance
• Chapter 10. Regression Analysis
• Chapter 11. Design of Experiment
• Chapter 12. The Taguchi Method
• Chapter 13. Measurement Systems Analysis–MSA: Is Your Measurement Process Lying to You?
• Chapter 14. Nonparametric Statistics
• Chapter 15. Pinpointing the Vital Few Root Causes

Let’s discuss a few of these chapters and determine if this book can assist you in your next Six Sigma project.  Chapter 2 discusses the basics of using Minitab and Excel.  There is a good amount of graphs and images so that you can clearly see what the author is discussing.  When discussing the capabilities of Excel, the author states “The flexibility of Excel’s macros has made it possible to create very rich and powerful statistical programs that have become widely used. In this book, we will only use the capabilities built into the basic Excel package. This will reduce the ability to perform some analyses with Excel but the use of a specific additional macro-generated program will require the user to purchase that program.”  The author also mentions “The tools under the Statistical category in the “Insert Function” dialog are only for basic probability and descriptive statistics; they are not fit for more complex data analysis. Analyses such as regression or ANOVA cannot be performed using the Insert Function tools. These are done through Data Analysis, which is an add-in that can be easily installed from the Tools menu.

Jumping to chapter 4, this is where the book introduces basic probability.  By the time you complete this chapter you should have an understand the meaning of probability, know how to use basic probability distributions, understand when to use a particular probability distribution, understand the concept of Rolled Throughput Yield and DPMO.  Discrete probability distributions are covered under section 4.` and the author does a good job explaining the important elements using tables.

Chapter 9 cover Analysis of Variance, this includes elements like ANOVA and Hypothesis Testing, Completely Randomized Experimental Design (One-Way ANOVA), and using both Excel and Minitab for these functions.  The chapter is flush with charts and examples to ensure you know how to handle any statistical calculation.  This area of the book will be intimidating for some users since advanced topics like t-tests and null hypotheses are covered.

The final chapter we reviewed was chapter 11, Design of Experiment.  MSI considers DOE to be one of the critical elements of a Six Sigma project, and one that isn’t given enough attention.  As the book states correctly, incorrect business decisions can have very serious consequences for a company.  ANOVA is a basic step in Design of Experiments.  It is a formidable tool for decision-making based on data analysis.  This is why ANOVA is covered in the previous chapter.  The Factorial Design with Two Factors is covered as well as how ANOVA can determine if the null hypothesis should be rejected.