Building on the foundation of business skills established in Phase One, Six Sigma Advanced Training builds measurement and analytical skills that enable more refined and sophisticated use of data. These become the "power tools" that Black Belts apply when charts, graphs and "descriptive" data methods fall short.

Concepts and tools covered includes:

• Measurement systems analysis (ensuring reliability of data to be analyzed)
  
  
• Testing for data normality and normality transforms
  
  
• Formal Null and Alternative Hypotheses
  
  
• Hypothesis testing tools, including Chi-square, t-tests, F- tests, ANOVA and Mood's median
  
  
• Correlation and regression tools, including simple linear, multiple and logistic regression
  

• Design of Experiments to assess the impact of key variables and optimize process performance
  
  
• Advanced control planning and various forms of control charts

Just as important as how to apply these analytical methods, Pivotal's Six Sigma Advanced Training emphasizes when and why statistical tools should be applied to add value and meaningful business knowledge.

Note: SSAT participants should be comfortable with mathematical calculations and basic use of spreadsheet software. Each participant should come with a laptop computer pre-installed with a designated statistical analysis software package for use during in-class exercises.

• Solidify understanding of fundamental principles of data and statistical analysis
  
  
• Enable effective decisions on measurement and data types needed for various forms of analysis
  
  
• Develop effective sampling plans and procedures according to analysis methods being applied
  
  
• Assess and manage data integrity, whether for projects or ongoing process metrics
  
  
• Organize/format data as needed to conduct meaningful analyses
  
  
• Identify and formulate critical questions or hypotheses to be investigated through statistical analysis and experimentation
  
  
• Teach participants a variety of valuable analytical methods, including their basic purpose and principles, when and where to use them, and how to execute the appropriate tests
  
  
• Build competence in appropriate software application(s) to support statistical tests
  
  
• Ensure effective interpretation of analysis results and appropriate follow-up