Module Details

Module Code: ZQUA C3103
Module Title: Quality Management, Experimental Design and Data Analysis
Title: Quality Management, Experimental Design and Data Analysis
Module Level:: 7
Credits:: 10
Module Coordinator: Paula Rankin
Module Author:: Rachael Carroll
Domains:  
Module Description: The aim of this module is to give students an overview of quality management systems and develop their understanding of statistical concepts and techniques as used in science and industry.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Describe the process model of quality, different quality management systems, standardisation, accreditation and continuous quality improvement methodologies.
LO2 Apply statistical tools to explore the relationship between variables and be able to interpret statistical information to be able to analyse data for problem solving.
LO3 Analyse a wide range of data from experiments. using laboratory practicals to demonstrate problem solving techniques and team working to analyse and interpret data. Both statistical and quality analysis tools will be developed.
LO4 Describe key elements required for consideration in the design of experiments and analyses experimental data.
LO5 Apply the strategies involved in lean and auditing and compliance within the Pharmaceutical and food sectors
LO6 Describe and discuss the role of Quality Systems, Documentation, Validation, Compliance and how Regulatory control fits into the Pharma sector.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is recommended before enrolment in this module.
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Additional Requisite Information
No Co Requisites listed
 
Indicative Content
Quality Management: Continuous Quality Improvement
Definitions of quality, quality control, quality assurance and quality management. Principles of a quality system, TQM and quality philosophies. Process model of quality and Quality by Design, consumer protection and product safety. Problem solving techniques for process improvement. Lean Six Sigma Methodology.
Quality Management:
Definitions of standards and standardisation. Rationale, development and structure of standards. Standards and Regulations. National and international bodies and schemes including NSAI, INAB, ISO, BRC and EIQA, method validation. Standards supporting innovation.
Quality Management: Quality Management Systems
ISO 9000 family. ISO 9001 and the seven quality management principles. Alignment of ISO 9001 with other standards, e.g. ISO 14001; Environmental Mgt Systems, ISO 22000; Food Safety Mgt Systems and ISO17025; General Requirements for the Competence of Calibration and Testing Laboratories.
Quality Management: Management
Levels of management, roles and responsibilities. Quality meetings, team building, team working, motivation, leadership and managing change. Project planning, setting objectives and meeting milestones. Producing deliverables and project evaluation.
Quality Management: Auditing
Internal and external auditing. Role of an auditor and the auditing team. Designing, planning and implementing an audit. Audit tools and checklists. Audit close out and management review.
Statistics and Experimental Design: Probability Essentials
Overview of continuous and discrete distributions including hypergeometric and negative binomial distributions. Conditional probability and Bayes Theorem.
Statistics and Experimental Design: Hypothesis testing
Review of formulation of hypotheses. Understanding p-values and statistical significance, one sample problems, confidence interval for the mean, one-sample Student’s t test.
Statistics and Experimental Design:Two sample Problems
Student’s t test for paired and unpaired situations. Matched and repeated measures designs.
Statistics and Experimental Design: Principles of experimental design
Principles of good data management and data visualization. Understanding and interpreting treatment effects. Introduction to experimental design. The analysis of variance (ANOVA) technique.
Statistics and Experimental Design: Many factors problems
Understand which type of analysis is appropriate to address specific research questions. Randomisation, replication and controls, use of random numbers in treatment allocation. Completely randomised design, blocking and randomised block design, Latin square designs, introduction to factorial experimental designs, two-factor ANOVA with and without replication
Module Content & Assessment
Assessment Breakdown%
Continuous Assessment10.00%
Practical50.00%
End of Module Formal Examination40.00%

Assessments

Full Time

Continuous Assessment
Assessment Type Examination % of Total Mark 10
Timing n/a Learning Outcomes 1,2,3,4,5,6
Non-marked No
Assessment Description
Quality management and experimental design CA exams and data analysis.
No Project
Practical
Assessment Type Practical/Skills Evaluation % of Total Mark 50
Timing n/a Learning Outcomes 1,2,3,4,5,6
Non-marked No
Assessment Description
Computer practicals and assessments.
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 40
Timing End-of-Semester Learning Outcomes 1,2,3,4,5,6
Non-marked No
Assessment Description
Written examination.
Reassessment Requirement
Exam Board
It is at the discretion of the Examination Board as to what the qualifying criteria are.

SETU Carlow Campus reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Category Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lecture 12 Weeks per Stage 4.00 48
Practicals Contact Practical class 12 Weeks per Stage 4.00 48
Estimated Learner Hours Non Contact No Description 15 Weeks per Stage 10.27 154
Total Weekly Contact Hours 8.00
 
Module Resources
Recommended Book Resources
  • Barrie G. Dale, David Bamford, Ton van der Wiele. (2016), Managing Quality: An Essential Guide and Resource Gateway, 6th. Wiley, [ISBN: 978-1-119-130].
  • NSAI. (2015), A World Built on Standards, A Textbook for Higher Education, Danish Standards Foundation, Denmark, [ISBN: 9788773109632].
  • Howard S. Gitlow, Richard J. Melnyck, David M. Levine. (2015), Guide to Six Sigma and Process Improvement for Practitioners and Students, A: Foundations, DMAIC, Tools, Cases, and Certification, 2nd. Pearson, [ISBN: 0133925366].
  • Paul Keller. (2011), Six Sigma Demystified, 2nd. McGraw-Hill, p.528, [ISBN: 9780071746793].
  • Michael L. George... [et al.]. (2005), The Lean Six Sigma Pocket Toolbook, 1st. McGraw-Hill, NY, [ISBN: 0071441190].
  • James T. McClave,Terry T Sincich. (2017), Statistics, Global Edition, Pearson Higher Ed, p.896, [ISBN: 9781292161563].
  • S. L. R. Ellison,Trevor J. Farrant,Vicki Barwick. (2009), Practical Statistics for the Analytical Scientist, Royal Society of Chemistry, p.268, [ISBN: 9780854041312].
  • Douglas C. Montgomery. (2020), Design and Analysis of Experiments, John Wiley & Sons, p.688, [ISBN: 1119722101].
  • James Miller,Jane C Miller. (2018), Statistics and Chemometrics for Analytical Chemistry, Pearson Higher Ed, p.312, [ISBN: 9781292186726].
  • S. L. R. Ellison,Trevor J. Farrant,Vicki Barwick. (2009), Practical Statistics for the Analytical Scientist, Royal Society of Chemistry, p.268, [ISBN: 0854041311].
  • Geoffrey M. Clarke. (1994), Statistics and experimental design, 3rd. E. Arnold,, [ISBN: 0340593245].
  • Ronald E. Walpole,Raymond H. Myers,Sharon L. Myers,Keying E. Ye. (2016), Probability & Statistics for Engineers & Scientists, MyStatLab, Global Edition, Pearson Higher Ed, p.816, [ISBN: 9781292161419].
  • Jim Fowler, Lou Cohen, and Phil Jarvis. (1998), Practical Satistics for Field Biology, Wiley, Chichester, [ISBN: 0471982962].
This module does not have any article/paper resources
Other Resources
Discussion Note: