QLTY07022 2019 Six Sigma 2 Statistical Control

General Details

Full Title
Six Sigma 2 Statistical Control
Transcript Title
Six Sigma 2 Statistical Contro
N/A %
Subject Area
QLTY - Quality
MEMA - Mech and Manufact Eng
07 - NFQ Level 7
05 - 05 Credits
Start Term
2019 - Full Academic Year 2019-20
End Term
9999 - The End of Time
Programme Membership
SG_EPLYP_J07 201900 Bachelor of Engineering in Polymer Processing (Add-on) SG_SQUAL_J07 201900 Bachelor of Science in Quality (Add-on) SG_EMANM_J07 201900 Bachelor of Engineering in Manufacturing Management (Add-on) SG_ELSIG_S07 201900 Certificate in Lean Sigma Quality SG_EPOLY_B07 201900 Bachelor of Engineering SG_EELEC_H08 202000 Bachelor of Engineering (Honours) in Electronics and Self Driving Technologies SG_EROBO_H08 202000 Bachelor of Engineering (Honours) in Robotics and Automation SG_EPOLP_J07 202200 Bachelor of Engineering in Polymer Process Engineering (Add-on) SG_EPOLZ_J07 202200 Bachelor of Engineering in Polymer Process Engineering (Add-on) SG_EPOLY_J07 202400 Bachelor of Engineering in Polymer Processing (Add-on)

This module aims to provide learners with the statistical tools associated with the six sigma DMAIC philosophy specifically in the areas of Measure, Improve and control consistent with the ASQ and Quality America Green Belt Body of Knowledge. The student will be able to perform basic statistical analysis, develop and plots control charts, determine process and measurement capability. Minitab statistical software will be used to demonstrate and apply these statistical techniques.

Learning Outcomes

On completion of this module the learner will/should be able to;


Perform basic probability calculations by applying the probability rules and concepts


Describe and interpret the common probability distributions


Calculate, analyse and interpret measurement systems


Perform process capability and process perfromance analysis and interpret the results.


Perform exploratory data analysis using multi-vari charts, correlation and simple regression


Perform basic hypothesis testing eg. Hypothesis testing of means


Perform one way analysis of variance


Describe the experimental design terms and process


Develop control charts for variable and attribute data and interpret the results


Develop control charts with Minitab


Use the six sigma tools to implement control and monitoring systems

Teaching and Learning Strategies

Real world examples, case studies and published peer reviewed papers will be utilised, where possible.

Minitab statistical software will be used to demonstrate and explain the statistical techniques.

Module Assessment Strategies

Assessments will be through a series of continuous assessments and a final written exam

Repeat Assessments


Indicative Syllabus

A. Probability and statistics

  • Drawing valid statistical conclusions
  • Distinguish between enumerative (descriptive) and analytical (inferential) studies, and distinguish between a population parameter and a sample statistic.
  • Central limit theorem and sampling distribution of the mean
  • Define the central limit theorem and describe its significance in the application of inferential statistics for confidence intervals, control charts, etc.
  • Basic probability concepts
  • Describe and apply concepts such as independence, mutually exclusive, multiplication rules, etc. 

B. Probability distributions

  •     Describe and interpret normal, binomial, and Poisson, chi square, Student's t, and F distributions.

C. Measurement system analysis

  • Calculate, analyze, and interpret measurement system capability using repeatability and reproducibility (GR&R), measurement correlation, bias, linearity, percent agreement, and precision/tolerance (P/T).

D. Process capability and performance

  • Process capability studies
  • Identify, describe, and apply the elements of designing and conducting process capability studies, including identifying characteristics, identifying specifications and tolerances, developing sampling plans, and verifying stability and normality.
  • Process performance vs. specification
  • Distinguish between natural process limits and specification limits, and calculate process performance metrics such as percent defective.
  • Process capability indices
  • Define, select, and calculate Cp and Cpk, and assess process capability.
  • Process performance indices
  • Define, select, and calculate Pp, Ppk, Cpm, and assess process performance.
  • Short-term vs. long-term capability
  • Describe the assumptions and conventions that are appropriate when only short-term data are collected and when only attributes data are available. Describe the changes in relationships that occur when long-term data are used, and interpret the relationship between long- and short-term capability as it relates to a 1.5 sigma shift.
  • Process capability for attributes data
  • Compute the sigma level for a process and describe its relationship to Ppk.

E. Exploratory data analysis

  • Multi-vari studies
  • Create and interpret multi-vari studies to interpret the difference between positional, cyclical, and temporal variation; apply sampling plans to investigate the largest sources of variation.
  • Simple linear correlation and regression
  • Interpret the correlation coefficient and determine its statistical significance (p-value); recognise the difference between correlation and causation. Interpret the linear regression equation and determine its statistical significance (p-value). Use regression models for estimation and prediction. 

F. Hypothesis testing

  • Basics
  • Define and distinguish between statistical and practical significance and apply tests for significance level.
  • Determine appropriate sample size for various test. .
  • Tests for means, and proportions
  • Define, compare, and contrast statistical and practical significance.
  • Single-factor analysis of variance (ANOVA)
  • Define terms related to one-way ANOVAs and interpret their results and data plots.
  • Chi square
  • Define and interpret chi square and use it to determine statistical significance.


G. Design of experiments (DOE)

  • Basic terms
  • Define and describe basic DOE terms such as independent and dependent variables, factors and levels, response, treatment, error, repetition, and replication.
  • Main effects
  • Interpret main effects and interaction plots.

H. Statistical process control (SPC)

  • Objectives and benefits
  • Describe the objectives and benefits of SPC, including controlling process performance, identifying
  • special and common causes, etc.
  • Rational subgrouping
  • Define and describe how rational subgrouping is used.
  • Selection and application of control charts
  • Identify, select, construct, and apply the following types of control charts: -R, -s, individuals and moving range (ImR / XmR), median, p, np, c, and u.
  • Analysis of control charts
  • Interpret control charts and distinguish between common and special causes using rules for determining statistical control.


I. Implement and validate solutions

  • Use various improvement methods such as brainstorming, main effects analysis, multi-vari studies, measurement system capability re-analysis, and post-improvement capability analysis to identify, implement, and validate solutions through F-test, t-test, etc .

J. Control plan

  • Assist in developing a control plan to document and hold the gains, and assist in implementing controls and monitoring systems.


Coursework & Assessment Breakdown

Coursework & Continuous Assessment
20 %
End of Semester / Year Formal Exam
80 %

Coursework Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Multiple Choice Quizzes Coursework Assessment UNKNOWN 10 % OnGoing 1,2,3,4,5,6,8,9,11
2 Assignment Work Coursework Assessment UNKNOWN 10 % OnGoing 4,7,9,10

End of Semester / Year Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Final Exam One 2.5 hour written paper Final Exam Closed Book Exam 80 % End of Term 1,2,3,4,5,6,7,8,9,11

Full Time Mode Workload

Type Location Description Hours Frequency Avg Workload
Lecture Not Specified Lecture 2 Weekly 2.00
Tutorial Not Specified Tutorial 2 Weekly 2.00
Independent Learning UNKNOWN Independent Learning 4 Weekly 4.00
Total Full Time Average Weekly Learner Contact Time 4.00 Hours

Part Time Mode Workload

Type Location Description Hours Frequency Avg Workload
Lecture Distance Learning Suite Online Lecture 2.5 Weekly 2.50
Tutorial Not Specified Tutorial 0 Weekly 0.00
Independent Learning UNKNOWN Independent Learning 4 Weekly 4.00
Total Part Time Average Weekly Learner Contact Time 2.50 Hours

Required & Recommended Book List

Required Reading
01/09/2017 Lean Six Sigma & Minitab - The Complete Toolbox Guide for Business Improvement Opex Resources Ltd

Module Resources

Non ISBN Literary Resources





Keller, Paul A.

Six Sigma Demystified 2nd Ed

ISBN: 007174679X

McGraw-Hill Professional


Oakland, John

Statistical Process Control



Roderick A. Munro, Matthew J. Maio, and Mohamed B. Nawaz The Certified Six Sigma Green Belt Handbook 2nd











Journal Resources


URL Resources


Other Resources


Additional Information