Multivariate Analysis and Attribute Data Analysis in Six Sigma

Candidates seeking Six Sigma Black Belt certification, quality professionals, engineers, production managers, frontline supervisors, and all individuals charged with responsibility for improving quality and processes at the organizational or departmental level, including process owners and champions

Proficiency at the Green Belt level with basic exploratory data analysis concepts as scoped in the ASQ – Six Sigma Green Belt Body of Knowledge (BOK)

Expected Duration
120 minutes

In 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. 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 SkillSoft’s ASQ-aligned Green Belt curriculum.


Multivariate Tools

  • perform aspects of a principal components analysis (PCA)
  • sequence the steps in a PCA
  • interpret factor scores as part of factor analysis (FA)
  • recognize the differences between factor analysis (FA) and principal components analysis (PCA)
  • interpret the results of a discriminant analysis
  • interpret the results of a multiple analysis of variance (MANOVA)

Analyzing Attribute Data

  • predict probability of the dependent variable for a given independent variable using logistic regression
  • calculate logit probability using the logit equation
  • interpret the results of the probit analysis of a given data sample





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