Basic Statistics and Graphical Methods for Six Sigma

Overview/Description
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.

Target Audience
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

Prerequisites
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 (hours)
2.0

Lesson Objectives

Basic Statistics and Graphical Methods for Six Sigma

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

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

recognize the significance of the central limit theorem for inferential statistics

match tools for drawing valid statistical conclusions to descriptions of their use