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Basics of Correlation, Regression, and Hypothesis Testing for Six Sigma

Target Audience
Expected Duration
Lesson Objectives
Course Number

During the Analyze stage of a Six Sigma project, teams use statistical tools such as correlation analysis, regression analysis, and hypothesis testing. An improvement project can greatly benefit from these tools. They provide information regarding which key inputs are affecting outputs and how to tweak inputs until the desired results are achieved. Then new results can then be tested to ensure the changes are due to the changes that were implemented. The course examines correlation analysis, regression analysis, and hypothesis testing. This course is aligned to the ASQ Body of Knowledge and is designed to assist Yellow Belt candidates toward their certification and also to become productive members on their Six Sigma project teams.

Target Audience
Candidates seeking Six Sigma Yellow Belt certification and any other individuals involved in quality and process improvement at the organizational or departmental level


Expected Duration (hours)

Lesson Objectives

Basics of Correlation, Regression, and Hypothesis Testing for Six Sigma

  • identify the purpose of correlation analysis in Six Sigma
  • interpret a scatter diagram to determine correlation
  • identify considerations when interpreting a correlation coefficient
  • recognize the importance of finding the statistical significance of correlation
  • demonstrate your understanding of how correlation is used during a Six Sigma project
  • recognize how linear regression is used during data analysis
  • calculate an outcome using the regression analysis formula
  • use the regression analysis formula
  • identify the purpose of hypothesis testing for means
  • identify properly worded hypothesis test results
  • recognize the attributes of Type I and Type II errors
  • match examples of alternate hypotheses with their corresponding probability distribution graphs
  • identify factors that affect the power of a hypothesis test
  • determine whether to reject a null hypothesis based on given p-values
  • demonstrate your understanding of hypothesis testing concepts
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