Basic Statistics and Graphical Methods for 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
120 minutes

Organizations must ensure that their processes and products are extremely consistent, as variations can lead to rejected orders, lower revenues, and eventually, financial disaster. Basic statistics can provide Black Belts with the tools to summarize and assess collected data in a meaningful way. 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 about the larger population based on their sample data, and can test the statistical validity of their inferences. Thus, basic statistics can provide an organization with a view of its performance in graphical format, and the tools for reaching valid conclusions regarding its processes and products.
This course provides Black Belts with basic statistical tools for describing, presenting, and analyzing sample and population 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.


Processing Data for 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 Statistical Conclusions

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





Multi-license discounts available for Annual and Monthly subscriptions.