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.
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