Course Bundle: Six Sigma Black Belt (2007 BOK): Analyze - 11.5 hours- "Correlation and Regression Analysis in Six Sigma" - 1.5 hours Item oper-16-a01-bs-enus
- "Multivariate Analysis and Attribute Data Analysis in Six Sigma" - 2.0 hours Item oper-16-a02-bs-enus
- "Hypothesis Testing Concepts and Tests for Means in Six Sigma" - 2.0 hours Item oper-16-a03-bs-enus
- "Tests for Variances and Proportions, ANOVA, and Chi-square Tests in Six Sigma" - 2.0 hours Item oper-16-a04-bs-enus
- "Nonparametric Tests in Six Sigma Analysis" - 2.0 hours Item oper-16-a05-bs-enus
- "Non-Statistical Analysis Methods in Six Sigma" - 2.0 hours Item No.oper-16-a06-bs-enus
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| Correlation and Regression Analysis in Six Sigma - 1.5 hoursAs a Six Sigma team moves into the Analyze stage of the DMAIC process, it looks more closely at the variables and variable interrelationships identified during the Measure stage. As part of the analysis, a scatter diagram of dependent and independent variables is drawn to visualize the form, strength, and direction of their relationships. By determining their correlation coefficient, a linear relationship can be quantified and identified as positive, negative, or neutral. Then, using regression analysis, a model is developed to describe the relationship as a linear equation and then used for predictions and estimations. However, it is essential to analyze the uncertainty in the estimate, to test that the relationship between variables is statistically significant, and that the model is valid. This course discusses two important tools – correlation and regression analysis for measuring and modeling relationships between variables. In terms of correlation, it takes learners through examp
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| Multivariate Analysis and Attribute Data Analysis in Six Sigma - 2.0 hoursIn the Analyze phase of the DMAIC methodology, a Six Sigma team begins to analyze the root causes of the problems that it identified in the earlier stages. This analysis may require churning out huge volumes of data of different types. Sometimes this data is of a multivariate nature, meaning that many dependent and independent variables need to be considered simultaneously. As such, Six Sigma teams often use advanced multivariate tools to manage this type of data. Data can also be of an attribute nature, for which Six Sigma teams use a different set of data analysis tools for analyzing and interpreting this type of data. This course deals with the tools used in Six Sigma for multivariate analysis and attribute data analysis. It discusses multivariate tools such as principal components, factor analysis, discriminant analysis, and multiple analysis of variance (MANOVA). It also deals with attribute data analysis using tools such as logistic regression, logit analysis, and probit analysis
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| Hypothesis Testing Concepts and Tests for Means in Six Sigma - 2 hoursIn the Analyze phase of the DMAIC methodology, Six Sigma teams analyze the underlying causes of issues that need to be addressed for the successful completion of their improvement projects. To that end, teams conduct a number of statistical analyses to determine the nature of variables and their interrelationships in the process under study. It is rarely possible to study and analyze the full scope of population data pertaining to all processes, products, or services, so Six Sigma teams typically collect samples of the population data to be analyzed, and based on that sample data, they make hypotheses about the entire population. Because there is a lot at stake in forming the correct conclusions about the larger population, Six Sigma teams validate their inferences using hypothesis tests. This course builds on basic hypothesis testing concepts, terminologies, and some of the most commonly used hypothesis tests – one- and two-sample tests for means. The course also discusses the importa
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| Tests for Variances and Proportions, ANOVA, and Chi-square Tests in Six Sigma - 2.0 hoursAs 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 learn
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| Nonparametric Tests in Six Sigma Analysis - 2.0 hoursHypothesis testing is a process of assuming an initial claim about the population characteristics and then statistically testing this claim using sample data. Testing hypotheses is a very important activity in Six Sigma projects in the areas of analysis, decision making, and change implementation. In conventional hypothesis tests – called parametric tests – a sample statistic is obtained to estimate a population parameter and hence requires a number of assumptions to be made about the underlying population; such as the normality of data. However, another category of hypothesis tests – called nonparametric tests – is used when some of these assumptions (such as normality of data) cannot be safely made. Nonparametric tests require fewer assumptions and are often used when the data is from an unknown or non-normal population. Nonparametric tests are not completely free from assumptions, however. For instance, they still require the data to be from an independent random sample. The course
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| Non-Statistical Analysis Methods in Six Sigma - 2.0 hoursGetting to the source of why something has gone wrong in a system or process is critical to identifying the changes necessary for resolving the problem. During the Analyze phase of a Six Sigma project, a Black Belt practitioner utilizes a variety of statistical and nonstatistical tools and methods for analyzing systems and processes to identify variation and defects, reduce costs, eliminate waste, and reduce cycle time. While many of the tools used in the Analyze phase are statistical and quantitative in nature, there are many useful nonstatistical methods. Nonstatistical methods help in the analysis by including qualitative considerations in identifying potential problems, their root causes, and their impacts. They help prioritize these causes and generate initial ideas for resolving problems when a project enters the Improve phase. This course covers the use of various nonstatistical analysis methods including failure modes and effects analysis (FMEA), gap analysis, scenario planning
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