In the Analyze phase of the DMAIC methodology, Six Sigma teams analyze the underlying causes of issues that need to be addressed for the successful completion of their improvement projects. To that end, teams conduct a number of statistical analyses to determine the nature of variables and their interrelationships in the process under study. It is rarely possible to study and analyze the full scope of population data pertaining to all processes, products, or services, so Six Sigma teams typically collect samples of the population data to be analyzed, and based on that sample data, they make hypotheses about the entire population. Because there is a lot at stake in forming the correct conclusions about the larger population, Six Sigma teams validate their inferences using hypothesis tests. This course builds on basic hypothesis testing concepts, terminologies, and some of the most commonly used hypothesis tests – one- and two-sample tests for means. The course also discusses the importance of sample size and power in hypothesis testing, as well as exploring issues relating to point estimators and confidence intervals in hypothesis testing. This course is aligned with the ASQ Certified Six Sigma Black Belt certification exam and is designed to assist learners as part of their exam preparation. It builds on foundational knowledge that is taught in SkillSoft's ASQ-aligned Green Belt curriculum.
Candidates seeking Six Sigma Black Belt certification, quality professionals, engineers, production managers, frontline supervisors, and all individuals charged with responsibility for improving quality and processes at the organizational or departmental level, including process owners and champions
Proficiency at the Green Belt level with basic hypothesis testing and test for means concepts as scoped in the ASQ - Six Sigma Green Belt Body of Knowledge (BOK)