Quality Science Introduction - Module

This module introduces and uses both basic and advanced statistics, appropriate for master black belt level Six Sigma, in a specialist area specific to the MSc Strategic Quality Management).

Delivery will include:

theory in quality science and statistical methods (generic)

practical application using software tools to undertake statistical analysis of domain specific problems, and

analysis of best practise case studies (domain specific).

On successful completion of this module students will be able to:

Apply the domain theory to the development of process understanding.

Assess multiple control options through iterative analysis.

Develop competence to work in a quality/ improvement team on a process understanding & control.

Describe methods of Statistical Process Control and explain the importance of and differences between Common and Special Cause Variation.

Appreciate the responsibilities and requirements of participating/leading process understanding and improvement control initiatives.

Subjects taught

Quality Science Introduction MS6041

The module will cover the following areas

Statistical thinking:
SIPOC Map, Top-Down and Deployment Flowcharts, brainstorming, multi-voting, affinity diagrams, cause-and-effect diagrams, 5 Whys, cause-and-effect matrices, nominal data, ordinal data, continuous data, descriptive statistics.

Exploratory Data Analysis:
exploratory data analysis (EDA), histogram, centring, spread, course shape, unusual pattern in EDA, bimodal distribution, interquartile range, median quartile, box plot, measures of spread, scatterplot, measurement system, input output process map, cause and effect diagram, measurement variation, data interaction, repeatability, reproducibility, bias, variance components analysis, variability gauge chart.

Quality Methods:
control chart, Central Limit Theorem, Rule of Averages, individual range, moving range, 3-way charts, special cause variation, rational subgrouping, I or MR charts, control charts with phases, the voice of the customer, process capability, the voice of the process, process capability indices: Cp, Cpl, Cpu, and Cpk, process stability, lower spec limit, upper spec limit, actual capability, actual performance, process performance indices, nonnormal data.

Decision Making with Data:
confidence interval, standard deviation, uncertainty, mean, outlier, variation, t-distribution, probability, tolerance interval, prediction interval, X-Bar charts, S or R charts, p-value, skewed data, nonparametric tolerance interval, observation, data specification, interval estimates, data target, hypothesis test, null hypothesis, alternative hypothesis, sampling distribution under the null, statistical significance, measurement systems analysis, one-way ANOVA, analysis of variance, F-Ratio, sum of squares for the model (SSM).

Entry requirements

Applicants must have a minimum second-class Level 8 honours degree (QQI NFQ or other internationally recognised equivalent) in a relevant or appropriate subject, or equivalent prior learning that is recognised by the University as meeting this requirement.

Successful completion of this module does not automatically qualify you for entry into a further award. All programme applicants must meet the entry requirements listed if applying for a further award.

Application dates

Please ensure you enter the Module Code below when applying for this MicroCred. Applications without this cannot be processed.

You may apply for more than one MicroCred under the same application.


15 weeks part-time.

Enrolment dates

Intake: Spring Autumn/Fall
Course Start Date: Spring 2024

Post Course Info

This micro-credential represents a single module within a larger further award (e.g., Certificate, Diploma, Masters). By taking this micro-credential you may be eligible to apply for a credit exemption should you progress to study for a further award.

The programmes associated with this MicroCred are:
MSc in Strategic Quality Management - Lean Sigma Systems

More details
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  • Attendance type

    Part time

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