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Hypothesis Tests for Variances and Proportions in Six Sigma


Overview/Description
Target Audience
Expected Duration
Lesson Objectives
Course Number



Overview/Description
As a Six Sigma project moves into the Analyze phase, team members identify possible sources of variation, underlying root causes, and areas for improvement. It is here where assumptions or hypotheses about a process, product, or service are made and validated using tests based on sample data. This course will familiarize you with some of the advanced hypothesis tests used in Six Sigma, such as test for proportions, variances, and analysis of variance (ANOVA). You will learn how to use Paired-comparison t-test and chi-square tests for validating hypotheses. 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)
1.9

Lesson Objectives

Hypothesis Tests for Variances and Proportions in Six Sigma

  • identify types of hypothesis tests
  • distinguish between examples of paired-comparison and two-sample t-tests
  • interpret the results of a given paired-comparison t-test using the critical value method
  • interpret the results of a given paired-comparison t-test using the p-value method
  • determine whether to accept a null hypothesis based on given paired-comparison t-test results
  • conduct a one-sample test for variance, given a scenario
  • conduct a one-sample test for variance
  • conduct a two-sample test for variance, given a scenario
  • conduct a two-sample test for variance
  • recognize the required sample size for a test for proportions, given the hypothesized proportion
  • conduct a one-sample test for proportion, given a scenario
  • conduct a two-sample test for proportion, given a scenario
  • conduct one- and two-sample tests for variance
  • match ANOVA concepts to their corresponding definitions
  • conduct a one-way ANOVA test, given a scenario
  • conduct a one-way ANOVA test for means
  • recognize valid parameters and interpretations related to chi-square tests
  • use a given contingency table to perform a chi-square test
  • conduct a chi-square test, given a scenario
  • Course Number:
    oper_27_a03_bs_enus