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Statistics and Graphical Presentation in Six Sigma

Basic graphs and tables can be used to summarize and assess performance-related data in a meaningful way. Six Sigma practitioners use descriptive statistics to tabulate and graphically represent sample data through a number of informative charts and diagrams. Using analytical statistics, inferences are made about the larger population based on their sample data. These tools allow the organization to view its performance graphically and draw valid conclusions about its processes and products. This course provides 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 to the ASQ Body of Knowledge and is designed to assist Green Belt candidates toward their certification and to become productive members on their Six Sigma project teams.

Target Audience
Candidates seeking Six Sigma Green Belt certification; also 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


Expected Duration (hours)

Lesson Objectives

Statistics and Graphical Presentation in Six Sigma

  • distinguish between the characteristics of descriptive and inferential statistics
  • recognize the implications of the central limit theorem for inferential statistics
  • match tools for inferential statistics to descriptions of their use
  • demonstrate your understanding of concepts related to inferential statistics
  • calculate measures of central tendency
  • calculate measures of dispersion
  • use measures of central tendency and dispersion, given a scenario
  • interpret a given frequency distribution table
  • calculate cumulative frequency distribution, given a dataset
  • calculate and use frequency distribution information on a Six Sigma project
  • match scatter diagrams with corresponding interpretations
  • interpret a given probability plot
  • recognize attributes of a process given a histogram
  • interpret a given stem-and-leaf plot
  • interpret a given box-and-whisker plot
  • interpret given graphical presentations



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