INTRODUCTION

One of the key things that sets the LSSBB apart from the SSGB is the depth of statistical knowledge the LSSBB has acquired. We recognize that many of the people who want to be able to classify themselves or be certified as a LSSBB are less interested in learning complex statistical tools like design of experiments. They may not even use them in the organizational assignment they have right now. However, at some later date they may change organizations or even jobs within the same organization, and if they include being certified as an LSSBB as one of their skills, they will need to understand these statistical tools or they will be misrepresenting themselves to their new employer.

One of the basic concepts of Six Sigma is collecting data and using hard data to make business and improvement decisions. We call it “managing by facts—not hunch.” All too often decisions are made based upon false or unfounded data that are presented to management as facts. Through the use of these statistical tools you will be able to qualify and quantify the information you are using to make business decisions.

Another reason you need to master the use of statistical problem-solving tools is because many, if not all, of the problems we face today are complex in nature. These problems are not caused by one root cause, but by the interaction of many root causes, and correcting only one of the root causes could even make the problem worse. Often taking action to solve each of the root causes independently becomes far more expensive than correcting how they interface and interact with each other. Through the use of tools like design of experiments, the Six Sigma Team can look at the interaction between the root causes and understand which root cause or combination of root causes has the biggest impact upon the problem they are analyzing. Relying on the hit-or-miss approach to solving these complex problems may take two to three times longer to come up with a solution that is only 50% as effective as the one you can get through the use of the statistical tools.

As an SSGB, you should have been introduced to the following statistical tools

1. Attributes control charts
2. Basic probability concepts
3. Basic statistical concepts
4. Binomial distribution
5. Chi-square test
6. Correlation coefficient
7. Cpk—Operational (long-term) process capability
8. Cpk—Using the K-factor method
9. CPU and CPL—upper and lower process capability indices
10. Data accuracy
11. Data, scale, and sources
12. Histograms
13. Hypothesis testing
14. Mean
15. Median
16. Mode
17. Mutually exclusive events
18. Normal distribution
19. Poisson distribution
20. Probability theory
21. Process capability analysis
22. Process capability study
23. Process elements, variables, and observations concepts
24. Range
25. Sampling
26. Six Sigma measures
27. Standard deviation
28. Statistical process control
29. Statistical thinking
30. Variables control charts
31. Variance

At this point you should review Table S5.1 to understand how each of the statistical tools can be used in the two major LSS methodologies.

To upgrade you to the LSSBB level in statistical knowledge, we will present the following statistical tools:

• ANOVA—one-way
• ANOVA—two-way
• Box plots
• Confidence intervals
• Data transformation
• Design of experiments
• Measurement system analysis
• Method of least squares
• Multivari charts
• Nonparametric statistical tests
• Population and samples
• Regression analysis
• Rolled-throughput yield
• Taguchi methods
• Validation

For ease of reference, the tools are presented in alphabetic order rather than in the order they would be used in solving a problem.