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As a Six Sigma team moves into the Analyze phase of a project, team members begin analyzing the information and data collected in the earlier phases. During the Analyze phase, Six Sigma teams 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 aims to familiarize you with some of the advanced hypothesis tests used in Six Sigma. You are taken through the key steps in testing hypotheses for proportions, variances, and analysis of variance (ANOVA), and their underlying assumptions, with the help of examples and case studies. You will also learn how to use goodness-of-fit test statistics and contingency tables for validating hypotheses about various aspects of the variables being analyzed. This course is aligned with the ASQ Certified Six Sigma Black Belt certification exam and is designed to assist learners as part of their exam preparation. It builds on foundational knowledge that is taught in 's ASQ-aligned Green Belt curriculum.

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Objectives

Tests for Variances and Proportions perform key steps in a hypothesis test for proportions, and interpret the results perform key steps in a one-sample hypothesis test for variance, and interpret the results distinguish between characteristics of one-sample tests for variance and two-sample tests for variance Analysis of Variance (ANOVA) perform key steps in a one-way ANOVA and interpret the results interpret results in a two-way ANOVA recognize examples of business problems that warrant a two-way ANOVA Goodness-of-fit Tests and Contingency Tables determine whether a goodness-of-fit test was calculated and interpreted correctly identify business problems or organizational questions that are suitable for a goodness-of-fit test use a contingency table to test the relationship between two variables identify statements that describe the purpose of contingency tables