Overview/Description
In the Analyze stage of the Six Sigma DMAIC process, you closely examine the output variable (known as y) and its possible causes or input variables (known as x's) collected in the Measure stage to get a deeper understanding of their relationships. The goal of this analysis is to narrow down the many possible x's identified earlier during the Measure stage, to a few probable ones. This analysis is generally conducted through the use of two important toolsets: exploratory data analysis and hypothesis testing. Methods and tools used in these broad toolsets help to identify a few probable root causes impacting process performance and the Six Sigma project goal. This course introduces some key exploratory data analysis tools used in Six Sigma such as multi-vari studies, correlation, and regression models. The course takes you through the multi-vari analysis to identify positional, cyclical, and temporal variations and how to apply an effective sampling plan for conducting this analysis. It also explains the correlation coefficient, its statistical significance, and how it is different from causation. In addition, the course helps you interpret the linear regression equation and explores how you can use it to model relationships for prediction and estimation of data. This course is aligned with the ASQ Certified Six Sigma Green Belt certification exam and is designed to assist learners as part of their exam preparation.

Target Audience
Candidates seeking Six Sigma Green Belt certification; quality professionals, engineers, production managers, and frontline supervisors; process owners and champions charged with the responsibility of improving quality and processes at the organizational or departmental level