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Multi-vari Studies, Correlation, and Linear Regression in Six Sigma

Target Audience
Expected Duration
Lesson Objectives
Course Number

In the Analyze stage of the Six Sigma DMAIC process, project teams carefully analyze process output and input variables. The goal of this analysis is to narrow down the many possible inputs identified during the Measure stage. The analysis is carried out using tools that help identify a few probable root causes that are impacting process performance. |n This course introduces some key tools used for exploratory data analysis in Six Sigma, such as multi-vari studies, correlation analysis, and regression models. It also explains the correlation coefficient and its statistical significance. In addition, the course helps you interpret the linear regression equation, understand the steps in hypothesis testing for regression statistics and explore the use of a regression model for prediction and estimation of outcomes. This course is aligned to the ASQ Body of Knowledge and is designed to assist Green Belt candidates toward achieving their certifications and becoming productive members of their Six Sigma project teams.

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

Expected Duration (hours)

Lesson Objectives

Multi-vari Studies, Correlation, and Linear Regression in Six Sigma

  • identify characteristics of a multi-vari analysis
  • identify guidelines for creating sampling plans
  • recognize actions involved in carrying out a multi-vari analysis
  • match variation types with corresponding characteristics
  • interpret given variation results
  • use variation results to determine the focus of a multi-vari study
  • identify uses of correlation analysis in Six Sigma
  • make inferences about data based on a given scatter diagram
  • recognize considerations for interpreting the correlation coefficient
  • determine the relationship between variables given scatter diagrams
  • identify characteristics of causation
  • identify reasons why Six Sigma teams should determine the statistical significance of a correlation coefficient
  • recognize the significance of determining causation and p-value for a set of variables
  • recognize how linear regression is used during data analysis
  • sequence the steps for hypothesis testing for regression statistics
  • calculate the t-statistic
  • calculate an outcome using the simple least-squares linear regression formula
  • use the p-value method to validate a hypothesis test for a given regression equation
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