Hypothesis Tests for Variances, Proportions, ANOVA, and Chi-Square in Six Sigma


Overview/Description
Target Audience
Expected Duration
Lesson Objectives
Course Number



Overview/Description
The hypothesis test is one of the most important tools used in the Analyze stage of the Six Sigma DMAIC methodology. A hypothesis test helps to determine whether or not an observed relationship or difference truly exists between inputs and outputs identified in the earlier stages of the process. Six Sigma teams are interested in determining whether this relationship or difference is due to random chance or if it is a true difference. If it is a real difference, Six Sigma teams like to determine if it has practical significance. The goal of this course is to explore several of the hypothesis tests used in Six Sigma. The course covers the key steps for testing hypotheses for proportions, variances, and paired comparisons with the help of real-life examples and case studies. It also covers how to use single-factor analysis of variance (ANOVA) and how to test hypotheses using a chi-square test. 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

Expected Duration (hours)
1.5

Lesson Objectives

Hypothesis Tests for Variances, Proportions, ANOVA, and Chi-Square in Six Sigma

  • identify some of the activities associated with testing hypotheses for variances
  • identify some of the activities associated with testing hypotheses for proportions
  • identify some of the activities associated with testing hypotheses using paired-comparison tests
  • recognize how to determine ANOVA figures in a given scenario
  • use a chi-square test statistic to determine statistical significance
  • Course Number:
    oper_08_a03_bs_enus