Six Sigma Green Belt Certification
Six Sigma Green Belt - Six Sigma Analyze
In the Analyze stage of the Six Sigma DMAIC process, you closely examine the output variable (known as y) and its possible causes or input variables (known as x's) collected in the Measure stage to get a deeper
understanding of their relationships. The goal of this analysis is to narrow down the many possible x's identified earlier during the Measure stage, to a few probable ones. This analysis is generally conducted
through the use of two important toolsets: exploratory data analysis and hypothesis testing. Methods and tools used in these broad toolsets help to identify a few probable root causes impacting process performance
and the Six Sigma project goal. This 5 hour program is aligned to the ASQ Certified Six Sigma Green Belt certification exam and is designed to assist learners as part of their exam preparation.
Six Sigma is a registered Trademark of Motorola Corporation, and all right, title, and interest in Six Sigma belongs to Motorola
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
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Six Sigma Green Belt - Analyze Training Curriculum Online
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sk6siganalygb
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$149.00
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Exploratory Data Analysis in Six Sigma
This course introduces some key exploratory data analysis tools used in Six Sigma such as multi-vari studies, correlation, and regression models. The course takes you through the multi-vari analysis to
identify positional, cyclical, and temporal variations and how to apply an effective sampling plan for conducting this analysis. It also explains the correlation coefficient, its statistical significance, and how it
is different from causation. In addition, the course helps you interpret the linear regression equation and explores how you can use it to model relationships for prediction and estimation of data.
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Introduction to Hypothesis Testing and Testing for Means in Six Sigma
The Analyze phase in Six Sigma closely examines the many process inputs identified in the Measure phase to determine if they are related to outputs, and if a relationship does exist, if it is statistically
significant. An important tool for this analysis is hypothesis testing. Hypothesis testing uses statistical analysis to determine if the observed relationship between two or more samples is real or due to random
chance. A variety of tests are used to find statistical evidence to reject or "not to reject" a hypothesis.
Once this is accomplished, the Six Sigma team is ready to move forward with identifying, testing, and implementing solutions to address the root causes of failure. This course covers the key hypothesis testing
concepts and the tests used in Six Sigma. The course will explore the steps for testing hypotheses for one-sample t-tests and two-sample t-tests with the help of real-life examples and case studies. The key terms
and the common procedures used to test hypotheses are also introduced
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Hypothesis Tests for Variances, Proportions, ANOVA, and Chi-Square
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.
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