QLTY07022 2019 Six Sigma 2 Statistical Control
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 multivari 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
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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.
 Shortterm vs. longterm capability
 Describe the assumptions and conventions that are appropriate when only shortterm data are collected and when only attributes data are available. Describe the changes in relationships that occur when longterm data are used, and interpret the relationship between long and shortterm 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
 Multivari studies
 Create and interpret multivari 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 (pvalue); recognise the difference between correlation and causation. Interpret the linear regression equation and determine its statistical significance (pvalue). 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.
 Singlefactor analysis of variance (ANOVA)
 Define terms related to oneway 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, multivari studies, measurement system capability reanalysis, and postimprovement capability analysis to identify, implement, and validate solutions through Ftest, ttest, 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 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 
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 
Required & Recommended Book List
01/09/2017 Lean Six Sigma & Minitab  The Complete Toolbox Guide for Business Improvement Opex Resources Ltd
Module Resources
Authors 
Title 
Publishers 
Year 
Keller, Paul A. 
Six Sigma Demystified 2nd Ed ISBN: 007174679X 
McGrawHill Professional 
2011 
Oakland, John 
Statistical Process Control 
Routledge 
2007 
Roderick A. Munro, Matthew J. Maio, and Mohamed B. Nawaz  The Certified Six Sigma Green Belt Handbook 2nd 
Pearson 
2015 








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