Summarizing and Presenting Data in Six Sigma

Overview/Description
Six Sigma teams use measures of central tendency and dispersion to reveal key facts about process data and the existing processes. They summarize data and put forth the relationships between various data components for further analysis. The teams then present these relationships in easy-to-understand graphical forms that facilitate comparison and help to identify possible trends. This course deals with the basic concepts of descriptive statistics, such as measures of central tendency and dispersion, and their significance in Six Sigma data analysis. The course also shows how to apply graphical methods, such as stem-and-leaf plots, box-and-whisker plots, run charts, and scatter diagrams, for illustrating relationships among various components of a given dataset. In addition, it examines how to depict distributions using histogram and normal probability plots. The course is aligned to the ASQ Certified Six Sigma Green Belt certification exam and is designed to assist learners as part of their exam preparation.

Target Audience
Candidates seeking Six Sigma Green Belt certification; 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)
1.5

Lesson Objectives

Summarizing and Presenting Data in Six Sigma

calculate the key measures of central tendency

calculate the key measures of dispersion

make key calculations in the creation of a cumulative frequency table

sequence the examples of steps for constructing a frequency distribution table

distinguish between the characteristics of stem-and-leaf plots, box-and-whisker plots, and Pareto charts

match run chart pattern names to their appropriate interpretations

identify statements that reflect correct interpretations of a scatter diagram

identify the general interpretations associated with the three types of histograms

sequence the examples of steps for creating a normal probability plot