Using Basic Statistics and Graphical Methods 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 Six Sigma basic statistics as scoped in the ASQ – Six Sigma Green Belt Body of Knowledge (BOK)

Expected Duration
121 minutes

Organizations must ensure that their products and services are extremely consistent to desired specifications, as variations can lead to rejected orders, reworks, and eventually, customer dissatisfaction and financial losses. Statistics can provide Black Belts with the tools to summarize and assess collected data in a meaningful way for identifying sources of variation and controlling them. Black Belts can use descriptive (enumerative) statistics to tabulate and graphically represent sample data through a number of informative charts and diagrams. Using analytical (inferential) statistics, supported by the central limit theorem, Black Belts can confidently make inferences, test the statistical validity of their inferences, and optimize and control processes.
This course provides Black Belts with basic statistical tools for describing, presenting, and analyzing data. It explores the process of preparing and presenting sample data using graphical methods and then making valid inferences about the population represented by the sample. 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.


Summarizing Data Using Descriptive Statistics

  • match measures of central tendency to their characteristic advantages and limitations
  • calculate measures of dispersion in a given scenario
  • construct a cumulative frequency diagram in a given scenario
  • recognize how to set class intervals for frequency distributions

Descriptive Statistics and Graphical Methods

  • predict and interpret the histogram shape that would result from a given frequency distribution
  • recognize how to use normal probability plots to determine whether data is normally distributed
  • identify statements that reflect correct interpretations of a complex box plot
  • identify the best interpretation of a given run chart
  • recognize how to use a scatter plot to find the optimum target value and tolerance zones for a process parameter

Analytical Statistics and Valid Conclusions

  • recognize the significance of the central limit theorem for inferential statistics
  • recognize the significance of central limit theorem in the application of hypothesis tests
  • match tools for drawing valid statistical conclusions to descriptions of their use





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